（1）前処理

1-1. ローデータの読み込み

#tidyverseパッケージ読み込み
library(tidyverse)

## -- Attaching packages ----------------------------------------- tidyverse 1.2.1 --

## √ ggplot2 2.2.1     √ purrr   0.2.5
## √ tibble  1.4.2     √ dplyr   0.7.6
## √ tidyr   0.8.1     √ stringr 1.3.0
## √ readr   1.1.1     √ forcats 0.3.0

## -- Conflicts -------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

#データ読み込み
lowdata <- read_csv("lowdata_4timepoints.csv", na = c(".", ""))

## Parsed with column specification:
## cols(
##   .default = col_integer()
## )

## See spec(...) for full column specifications.

lowdata$gender_T1 <- factor(lowdata$gender_T1) #性別をfactor型に変換
lowdata$gender_T2 <- factor(lowdata$gender_T2) #性別をfactor型に変換
lowdata$gender_T3 <- factor(lowdata$gender_T3) #性別をfactor型に変換
lowdata$gender_T4 <- factor(lowdata$gender_T4) #性別をfactor型に変換
head(lowdata) #先頭6行確認

names(lowdata) #変数名確認

##  [1] "ID"        "school"    "grade"     "age_T1"    "gender_T1"
##  [6] "hsc1_T1"   "hsc2_T1"   "hsc3_T1"   "hsc4_T1"   "hsc5_T1"  
## [11] "hsc6_T1"   "hsc7_T1"   "hsc8_T1"   "hsc9_T1"   "hsc10_T1" 
## [16] "hsc11_T1"  "hsc12_T1"  "wb1_T1"    "wb2_T1"    "wb3_T1"   
## [21] "wb4_T1"    "wb5_T1"    "ev1_T1"    "ev2_T1"    "age_T2"   
## [26] "gender_T2" "hsc1_T2"   "hsc2_T2"   "hsc3_T2"   "hsc4_T2"  
## [31] "hsc5_T2"   "hsc6_T2"   "hsc7_T2"   "hsc8_T2"   "hsc9_T2"  
## [36] "hsc10_T2"  "hsc11_T2"  "hsc12_T2"  "wb1_T2"    "wb2_T2"   
## [41] "wb3_T2"    "wb4_T2"    "wb5_T2"    "ev1_T2"    "ev2_T2"   
## [46] "age_T3"    "gender_T3" "hsc1_T3"   "hsc2_T3"   "hsc3_T3"  
## [51] "hsc4_T3"   "hsc5_T3"   "hsc6_T3"   "hsc7_T3"   "hsc8_T3"  
## [56] "hsc9_T3"   "hsc10_T3"  "hsc11_T3"  "hsc12_T3"  "wb1_T3"   
## [61] "wb2_T3"    "wb3_T3"    "wb4_T3"    "wb5_T3"    "ev1_T3"   
## [66] "ev2_T3"    "age_T4"    "gender_T4" "hsc1_T4"   "hsc2_T4"  
## [71] "hsc3_T4"   "hsc4_T4"   "hsc5_T4"   "hsc6_T4"   "hsc7_T4"  
## [76] "hsc8_T4"   "hsc9_T4"   "hsc10_T4"  "hsc11_T4"  "hsc12_T4" 
## [81] "wb1_T4"    "wb2_T4"    "wb3_T4"    "wb4_T4"    "wb5_T4"   
## [86] "ev1_T4"    "ev2_T4"

1-2. 下位尺度得点作成

data <- lowdata %>% 
  dplyr::mutate(eoe_T1 = (hsc4_T1 + hsc6_T1 + hsc8_T1 + hsc9_T1 + hsc12_T1)/5, na.rm = TRUE) %>% #EOE_T1の平均
  dplyr::mutate(lst_T1 = (hsc2_T1 + hsc11_T1)/2, na.rm = TRUE) %>% #LST_T1の平均
  dplyr::mutate(aes_T1 = (hsc5_T1 + hsc10_T1 + hsc1_T1 + hsc3_T1)/4, na.rm = TRUE) %>% #AES_T1の平均
  dplyr::mutate(hsc_T1 = (eoe_T1 + lst_T1 + aes_T1)/3, na.rm = TRUE) %>% #HSC_T1の平均
  dplyr::mutate(eoe_T2 = (hsc4_T2 + hsc6_T2 + hsc8_T2 + hsc9_T2 + hsc12_T2)/5, na.rm = TRUE) %>% #EOE_T2の平均
  dplyr::mutate(lst_T2 = (hsc2_T2 + hsc11_T2)/2, na.rm = TRUE) %>% #LST_T2の平均
  dplyr::mutate(aes_T2 = (hsc5_T2 + hsc10_T2 + hsc1_T2 + hsc3_T2)/4, na.rm = TRUE) %>% #AES_T2の平均
  dplyr::mutate(hsc_T2 = (eoe_T2 + lst_T2 + aes_T2)/3, na.rm = TRUE)  %>% #HSC_T2の平均
  dplyr::mutate(eoe_T3 = (hsc4_T3 + hsc6_T3 + hsc8_T3 + hsc9_T3 + hsc12_T3)/5, na.rm = TRUE) %>% #EOE_T3の平均
  dplyr::mutate(lst_T3 = (hsc2_T3 + hsc11_T3)/2, na.rm = TRUE) %>% #LST_T3の平均
  dplyr::mutate(aes_T3 = (hsc5_T3 + hsc10_T3 + hsc1_T3 + hsc3_T3)/4, na.rm = TRUE) %>% #AES_T3の平均
  dplyr::mutate(hsc_T3 = (eoe_T3 + lst_T3 + aes_T3)/3, na.rm = TRUE) %>% #HSC_T3の平均
  dplyr::mutate(eoe_T4 = (hsc4_T4 + hsc6_T4 + hsc8_T4 + hsc9_T4 + hsc12_T4)/5, na.rm = TRUE) %>% #EOE_T4の平均
  dplyr::mutate(lst_T4 = (hsc2_T4 + hsc11_T4)/2, na.rm = TRUE) %>% #LST_T4の平均
  dplyr::mutate(aes_T4 = (hsc5_T4 + hsc10_T4 + hsc1_T4 + hsc3_T4)/4, na.rm = TRUE) %>% #AES_T4の平均
  dplyr::mutate(hsc_T4 = (eoe_T4 + lst_T4 + aes_T4)/3, na.rm = TRUE) %>% #HSC_T4の平均
  dplyr::mutate(hsc_onemonth = (hsc_T1 + hsc_T2 + hsc_T3 + hsc_T4)/4, na.rm = TRUE) %>% #1ヵ月間のHSC平均
  dplyr::mutate(wb_T1 = (wb1_T1 + wb2_T1 + wb3_T1 + wb4_T1 + wb5_T1)/5, na.rm = TRUE) %>% #wb_T1の平均
  dplyr::mutate(wb_T2 = (wb1_T2 + wb2_T2 + wb3_T2 + wb4_T2 + wb5_T2)/5, na.rm = TRUE) %>% #wb_T2の平均
  dplyr::mutate(wb_T3 = (wb1_T3 + wb2_T3 + wb3_T3 + wb4_T3 + wb5_T3)/5, na.rm = TRUE) %>% #wb_T3の平均
  dplyr::mutate(wb_T4 = (wb1_T4 + wb2_T4 + wb3_T4 + wb4_T4 + wb5_T4)/5, na.rm = TRUE) %>% #wb_T4の平均
  dplyr::mutate(wb_onemonth = (wb_T1 + wb_T2 + wb_T3 + wb_T4)/4, na.rm = TRUE) %>% #1か月間のwb平均
  dplyr::mutate(ev_T1 = (as.numeric(ev1_T1) + as.numeric(ev2_T1))/2, na.rm = TRUE) %>% #event_T1の平均 #as.numericにしないとエラーが出る
  dplyr::mutate(ev_T2 = (as.numeric(ev1_T2) + as.numeric(ev2_T2))/2, na.rm = TRUE) %>% #event_T2の平均
  dplyr::mutate(ev_T3 = (as.numeric(ev1_T3) + as.numeric(ev2_T3))/2, na.rm = TRUE) %>% #event_T3の平均
  dplyr::mutate(ev_T4 = (as.numeric(ev1_T4) + as.numeric(ev2_T4))/2, na.rm = TRUE) %>% #event_T4の平均
  dplyr::mutate(ev_onemonth = (ev_T1 + ev_T2 + ev_T3 + ev_T4)/4, na.rm = TRUE) %>% #1か月間のev平均
  dplyr::select(-na.rm) #謎にna.rmという変数が勝手に作成されてしまうのでそれを除外

head(data) #先頭6行確認

names(data) #変数名確認

##   [1] "ID"           "school"       "grade"        "age_T1"      
##   [5] "gender_T1"    "hsc1_T1"      "hsc2_T1"      "hsc3_T1"     
##   [9] "hsc4_T1"      "hsc5_T1"      "hsc6_T1"      "hsc7_T1"     
##  [13] "hsc8_T1"      "hsc9_T1"      "hsc10_T1"     "hsc11_T1"    
##  [17] "hsc12_T1"     "wb1_T1"       "wb2_T1"       "wb3_T1"      
##  [21] "wb4_T1"       "wb5_T1"       "ev1_T1"       "ev2_T1"      
##  [25] "age_T2"       "gender_T2"    "hsc1_T2"      "hsc2_T2"     
##  [29] "hsc3_T2"      "hsc4_T2"      "hsc5_T2"      "hsc6_T2"     
##  [33] "hsc7_T2"      "hsc8_T2"      "hsc9_T2"      "hsc10_T2"    
##  [37] "hsc11_T2"     "hsc12_T2"     "wb1_T2"       "wb2_T2"      
##  [41] "wb3_T2"       "wb4_T2"       "wb5_T2"       "ev1_T2"      
##  [45] "ev2_T2"       "age_T3"       "gender_T3"    "hsc1_T3"     
##  [49] "hsc2_T3"      "hsc3_T3"      "hsc4_T3"      "hsc5_T3"     
##  [53] "hsc6_T3"      "hsc7_T3"      "hsc8_T3"      "hsc9_T3"     
##  [57] "hsc10_T3"     "hsc11_T3"     "hsc12_T3"     "wb1_T3"      
##  [61] "wb2_T3"       "wb3_T3"       "wb4_T3"       "wb5_T3"      
##  [65] "ev1_T3"       "ev2_T3"       "age_T4"       "gender_T4"   
##  [69] "hsc1_T4"      "hsc2_T4"      "hsc3_T4"      "hsc4_T4"     
##  [73] "hsc5_T4"      "hsc6_T4"      "hsc7_T4"      "hsc8_T4"     
##  [77] "hsc9_T4"      "hsc10_T4"     "hsc11_T4"     "hsc12_T4"    
##  [81] "wb1_T4"       "wb2_T4"       "wb3_T4"       "wb4_T4"      
##  [85] "wb5_T4"       "ev1_T4"       "ev2_T4"       "eoe_T1"      
##  [89] "lst_T1"       "aes_T1"       "hsc_T1"       "eoe_T2"      
##  [93] "lst_T2"       "aes_T2"       "hsc_T2"       "eoe_T3"      
##  [97] "lst_T3"       "aes_T3"       "hsc_T3"       "eoe_T4"      
## [101] "lst_T4"       "aes_T4"       "hsc_T4"       "hsc_onemonth"
## [105] "wb_T1"        "wb_T2"        "wb_T3"        "wb_T4"       
## [109] "wb_onemonth"  "ev_T1"        "ev_T2"        "ev_T3"       
## [113] "ev_T4"        "ev_onemonth"

# write.csv(data, file = "data_for_analysis.csv", na = ".") #csvで書き出し

1-3. 度数分布とヒストグラム

library(plotly)

#性別T1の度数分布とヒストグラム
gender_T1_count <- dplyr::count(data, gender_T1)
knitr::kable(gender_T1_count) #テーブル化

gender_T1	n
0	43
1	71

a <- ggplot(data = data, mapping = aes(x = gender_T1, fill = factor(gender_T1))) + geom_bar() #視覚化
ggplotly(a) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#性別T2の度数分布とヒストグラム
gender_T2_count <- dplyr::count(data, gender_T2)
knitr::kable(gender_T2_count) #テーブル化

gender_T2	n
0	38
1	62
NA	14

b <- ggplot(data = data, mapping = aes(x = gender_T2, fill = factor(gender_T2))) + geom_bar() #視覚化
ggplotly(b) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#性別T3の度数分布とヒストグラム
gender_T3_count <- dplyr::count(data, gender_T3)
knitr::kable(gender_T3_count) #テーブル化

gender_T3	n
0	38
1	67
NA	9

c <- ggplot(data = data, mapping = aes(x = gender_T3, fill = factor(gender_T3))) + geom_bar() #視覚化
ggplotly(c) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#性別T4の度数分布とヒストグラム
gender_T4_count <- dplyr::count(data, gender_T4)
knitr::kable(gender_T4_count) #テーブル化

gender_T4	n
0	39
1	67
NA	8

d <- ggplot(data = data, mapping = aes(x = gender_T4, fill = factor(gender_T4))) + geom_bar() #視覚化
ggplotly(d) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc1_T1の度数分布とヒストグラム
hsc1_T1_count <- dplyr::count(data, hsc1_T1)
knitr::kable(hsc1_T1_count) #テーブル化

hsc1_T1	n
1	2
2	8
3	10
4	16
5	48
6	19
7	11

e <- ggplot(data = data, mapping = aes(x = hsc1_T1, fill = factor(hsc1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(e) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc2_T1の度数分布とヒストグラム
hsc2_T1_count <- dplyr::count(data, hsc2_T1)
knitr::kable(hsc2_T1_count) #テーブル化

hsc2_T1	n
1	3
2	12
3	10
4	20
5	29
6	24
7	16

f <- ggplot(data = data, mapping = aes(x = hsc2_T1, fill = factor(hsc2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(f) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc3_T1の度数分布とヒストグラム
hsc3_T1_count <- dplyr::count(data, hsc3_T1)
knitr::kable(hsc3_T1_count) #テーブル化

hsc3_T1	n
2	4
3	4
4	12
5	23
6	35
7	35
NA	1

g <- ggplot(data = data, mapping = aes(x = hsc3_T1, fill = factor(hsc3_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(g) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc4_T1の度数分布とヒストグラム
hsc4_T1_count <- dplyr::count(data, hsc4_T1)
knitr::kable(hsc4_T1_count) #テーブル化

hsc4_T1	n
1	8
2	13
3	13
4	19
5	28
6	17
7	16

h <- ggplot(data = data, mapping = aes(x = hsc4_T1, fill = factor(hsc4_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(h) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc5_T1の度数分布とヒストグラム
hsc5_T1_count <- dplyr::count(data, hsc5_T1)
knitr::kable(hsc5_T1_count) #テーブル化

hsc5_T1	n
3	1
4	4
5	21
6	36
7	52

i <- ggplot(data = data, mapping = aes(x = hsc5_T1, fill = factor(hsc5_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(i) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc6_T1の度数分布とヒストグラム
hsc6_T1_count <- dplyr::count(data, hsc6_T1)
knitr::kable(hsc6_T1_count) #テーブル化

hsc6_T1	n
1	2
2	5
3	12
4	21
5	35
6	25
7	14

j <- ggplot(data = data, mapping = aes(x = hsc6_T1, fill = factor(hsc6_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(j) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc7_T1の度数分布とヒストグラム
hsc7_T1_count <- dplyr::count(data, hsc7_T1)
knitr::kable(hsc7_T1_count) #テーブル化

hsc7_T1	n
1	4
2	11
3	13
4	21
5	17
6	26
7	22

k <- ggplot(data = data, mapping = aes(x = hsc7_T1, fill = factor(hsc7_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(k) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc8_T1の度数分布とヒストグラム
hsc8_T1_count <- dplyr::count(data, hsc8_T1)
knitr::kable(hsc8_T1_count) #テーブル化

hsc8_T1	n
1	3
2	16
3	12
4	42
5	24
6	13
7	4

l <- ggplot(data = data, mapping = aes(x = hsc8_T1, fill = factor(hsc8_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(l) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc9_T1の度数分布とヒストグラム
hsc9_T1_count <- dplyr::count(data, hsc9_T1)
knitr::kable(hsc9_T1_count) #テーブル化

hsc9_T1	n
1	9
2	21
3	18
4	38
5	17
6	10
7	1

m <- ggplot(data = data, mapping = aes(x = hsc9_T1, fill = factor(hsc9_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(m) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc10_T1の度数分布とヒストグラム
hsc10_T1_count <- dplyr::count(data, hsc10_T1)
knitr::kable(hsc10_T1_count) #テーブル化

hsc10_T1	n
4	3
5	10
6	29
7	72

n <- ggplot(data = data, mapping = aes(x = hsc10_T1, fill = factor(hsc10_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(n) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc11_T1の度数分布とヒストグラム
hsc11_T1_count <- dplyr::count(data, hsc11_T1)
knitr::kable(hsc11_T1_count) #テーブル化

hsc11_T1	n
1	2
2	5
3	12
4	25
5	26
6	23
7	21

o <- ggplot(data = data, mapping = aes(x = hsc11_T1, fill = factor(hsc11_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(o) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc12_T1の度数分布とヒストグラム
hsc12_T1_count <- dplyr::count(data, hsc12_T1)
knitr::kable(hsc12_T1_count) #テーブル化

hsc12_T1	n
1	2
2	5
3	5
4	2
5	28
6	37
7	35

p <- ggplot(data = data, mapping = aes(x = hsc12_T1, fill = factor(hsc12_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(p) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc1_T2の度数分布とヒストグラム
hsc1_T2_count <- dplyr::count(data, hsc1_T2)
knitr::kable(hsc1_T2_count) #テーブル化

hsc1_T2	n
1	2
2	4
3	10
4	20
5	37
6	22
7	5
NA	14

q <- ggplot(data = data, mapping = aes(x = hsc1_T2, fill = factor(hsc1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(q) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc2_T2の度数分布とヒストグラム
hsc2_T2_count <- dplyr::count(data, hsc2_T2)
knitr::kable(hsc2_T2_count) #テーブル化

hsc2_T2	n
1	1
2	8
3	12
4	13
5	30
6	24
7	12
NA	14

r <- ggplot(data = data, mapping = aes(x = hsc2_T2, fill = factor(hsc2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(r) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc3_T2の度数分布とヒストグラム
hsc3_T2_count <- dplyr::count(data, hsc3_T2)
knitr::kable(hsc3_T2_count) #テーブル化

hsc3_T2	n
1	1
2	2
3	3
4	11
5	22
6	33
7	28
NA	14

s <- ggplot(data = data, mapping = aes(x = hsc3_T2, fill = factor(hsc3_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(s) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc4_T2の度数分布とヒストグラム
hsc4_T2_count <- dplyr::count(data, hsc4_T2)
knitr::kable(hsc4_T2_count) #テーブル化

hsc4_T2	n
1	7
2	11
3	10
4	11
5	27
6	25
7	9
NA	14

t <- ggplot(data = data, mapping = aes(x = hsc4_T2, fill = factor(hsc4_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(t) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc5_T2の度数分布とヒストグラム
hsc5_T2_count <- dplyr::count(data, hsc5_T2)
knitr::kable(hsc5_T2_count) #テーブル化

hsc5_T2	n
3	2
4	6
5	16
6	30
7	46
NA	14

u <- ggplot(data = data, mapping = aes(x = hsc5_T2, fill = factor(hsc5_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(u) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc6_T2の度数分布とヒストグラム
hsc6_T2_count <- dplyr::count(data, hsc6_T2)
knitr::kable(hsc6_T2_count) #テーブル化

hsc6_T2	n
1	1
2	8
3	8
4	13
5	40
6	20
7	10
NA	14

v <- ggplot(data = data, mapping = aes(x = hsc6_T2, fill = factor(hsc6_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(v) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc7_T2の度数分布とヒストグラム
hsc7_T2_count <- dplyr::count(data, hsc7_T2)
knitr::kable(hsc7_T2_count) #テーブル化

hsc7_T2	n
1	3
2	9
3	9
4	23
5	10
6	27
7	19
NA	14

w <- ggplot(data = data, mapping = aes(x = hsc7_T2, fill = factor(hsc7_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(w) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc8_T2の度数分布とヒストグラム
hsc8_T2_count <- dplyr::count(data, hsc8_T2)
knitr::kable(hsc8_T2_count) #テーブル化

hsc8_T2	n
2	11
3	16
4	23
5	31
6	13
7	5
NA	15

neko <- ggplot(data = data, mapping = aes(x = hsc8_T2, fill = factor(hsc8_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(neko) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc9_T2の度数分布とヒストグラム
hsc9_T2_count <- dplyr::count(data, hsc9_T2)
knitr::kable(hsc9_T2_count) #テーブル化

hsc9_T2	n
1	4
2	20
3	15
4	30
5	17
6	9
7	5
NA	14

y <- ggplot(data = data, mapping = aes(x = hsc9_T2, fill = factor(hsc9_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(y) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc10_T2の度数分布とヒストグラム
hsc10_T2_count <- dplyr::count(data, hsc10_T2)
knitr::kable(hsc10_T2_count) #テーブル化

hsc10_T2	n
2	1
3	2
4	1
5	12
6	31
7	53
NA	14

z <- ggplot(data = data, mapping = aes(x = hsc10_T2, fill = factor(hsc10_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(z) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc11_T2の度数分布とヒストグラム
hsc11_T2_count <- dplyr::count(data, hsc11_T2)
knitr::kable(hsc11_T2_count) #テーブル化

hsc11_T2	n
1	2
2	10
3	11
4	14
5	30
6	18
7	14
NA	15

aa <- ggplot(data = data, mapping = aes(x = hsc11_T2, fill = factor(hsc11_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aa) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc12_T2の度数分布とヒストグラム
hsc12_T2_count <- dplyr::count(data, hsc12_T2)
knitr::kable(hsc12_T2_count) #テーブル化

hsc12_T2	n
1	1
2	5
3	3
4	9
5	22
6	36
7	24
NA	14

bb <- ggplot(data = data, mapping = aes(x = hsc12_T2, fill = factor(hsc12_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bb) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc1_T3の度数分布とヒストグラム
hsc1_T3_count <- dplyr::count(data, hsc1_T3)
knitr::kable(hsc1_T3_count) #テーブル化

hsc1_T3	n
1	1
2	3
3	12
4	19
5	40
6	24
7	6
NA	9

cc <- ggplot(data = data, mapping = aes(x = hsc1_T3, fill = factor(hsc1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(cc) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc2_T3の度数分布とヒストグラム
hsc2_T3_count <- dplyr::count(data, hsc2_T3)
knitr::kable(hsc2_T3_count) #テーブル化

hsc2_T3	n
1	2
2	5
3	9
4	11
5	28
6	35
7	15
NA	9

dd <- ggplot(data = data, mapping = aes(x = hsc2_T3, fill = factor(hsc2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dd) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc3_T3の度数分布とヒストグラム
hsc3_T3_count <- dplyr::count(data, hsc3_T3)
knitr::kable(hsc3_T3_count) #テーブル化

hsc3_T3	n
2	1
3	2
4	6
5	23
6	29
7	44
NA	9

ff <- ggplot(data = data, mapping = aes(x = hsc3_T3, fill = factor(hsc3_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ff) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc4_T3の度数分布とヒストグラム
hsc4_T3_count <- dplyr::count(data, hsc4_T3)
knitr::kable(hsc4_T3_count) #テーブル化

hsc4_T3	n
1	3
2	7
3	3
4	17
5	37
6	24
7	14
NA	9

gg <- ggplot(data = data, mapping = aes(x = hsc4_T3, fill = factor(hsc4_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(gg) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc5_T3の度数分布とヒストグラム
hsc5_T3_count <- dplyr::count(data, hsc5_T3)
knitr::kable(hsc5_T3_count) #テーブル化

hsc5_T3	n
2	2
3	2
4	4
5	15
6	24
7	58
NA	9

hh <- ggplot(data = data, mapping = aes(x = hsc5_T3, fill = factor(hsc5_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hh) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc6_T3の度数分布とヒストグラム
hsc6_T3_count <- dplyr::count(data, hsc6_T3)
knitr::kable(hsc6_T3_count) #テーブル化

hsc6_T3	n
1	2
2	2
3	5
4	14
5	31
6	33
7	17
NA	10

ii <- ggplot(data = data, mapping = aes(x = hsc6_T3, fill = factor(hsc6_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ii) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc7_T3の度数分布とヒストグラム
hsc7_T3_count <- dplyr::count(data, hsc7_T3)
knitr::kable(hsc7_T3_count) #テーブル化

hsc7_T3	n
1	5
2	11
3	8
4	22
5	13
6	23
7	23
NA	9

jj <- ggplot(data = data, mapping = aes(x = hsc7_T3, fill = factor(hsc7_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jj) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc8_T3の度数分布とヒストグラム
hsc8_T3_count <- dplyr::count(data, hsc8_T3)
knitr::kable(hsc8_T3_count) #テーブル化

hsc8_T3	n
1	2
2	5
3	15
4	23
5	29
6	18
7	13
NA	9

kk <- ggplot(data = data, mapping = aes(x = hsc8_T3, fill = factor(hsc8_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kk) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc9_T3の度数分布とヒストグラム
hsc9_T3_count <- dplyr::count(data, hsc9_T3)
knitr::kable(hsc9_T3_count) #テーブル化

hsc9_T3	n
1	6
2	15
3	16
4	29
5	25
6	10
7	4
NA	9

ll <- ggplot(data = data, mapping = aes(x = hsc9_T3, fill = factor(hsc9_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ll) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc10_T3の度数分布とヒストグラム
hsc10_T3_count <- dplyr::count(data, hsc10_T3)
knitr::kable(hsc10_T3_count) #テーブル化

hsc10_T3	n
3	1
4	3
5	10
6	29
7	62
NA	9

dog <- ggplot(data = data, mapping = aes(x = hsc10_T3, fill = factor(hsc10_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dog) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc11_T3の度数分布とヒストグラム
hsc11_T3_count <- dplyr::count(data, hsc11_T3)
knitr::kable(hsc11_T3_count) #テーブル化

hsc11_T3	n
1	3
2	4
3	10
4	22
5	25
6	25
7	16
NA	9

mm <- ggplot(data = data, mapping = aes(x = hsc11_T3, fill = factor(hsc11_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mm) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc12_T3の度数分布とヒストグラム
hsc12_T3_count <- dplyr::count(data, hsc12_T3)
knitr::kable(hsc12_T3_count) #テーブル化

hsc12_T3	n
2	1
3	2
4	9
5	27
6	39
7	27
NA	9

nn <- ggplot(data = data, mapping = aes(x = hsc12_T3, fill = factor(hsc12_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(nn) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc1_T4の度数分布とヒストグラム
hsc1_T4_count <- dplyr::count(data, hsc1_T4)
knitr::kable(hsc1_T4_count) #テーブル化

hsc1_T4	n
1	1
2	2
3	15
4	17
5	41
6	27
7	3
NA	8

oo <- ggplot(data = data, mapping = aes(x = hsc1_T4, fill = factor(hsc1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(oo) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc2_T4の度数分布とヒストグラム
hsc2_T4_count <- dplyr::count(data, hsc2_T4)
knitr::kable(hsc2_T4_count) #テーブル化

hsc2_T4	n
2	9
3	11
4	9
5	32
6	32
7	13
NA	8

pp <- ggplot(data = data, mapping = aes(x = hsc2_T4, fill = factor(hsc2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(pp) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc3_T4の度数分布とヒストグラム
hsc3_T4_count <- dplyr::count(data, hsc3_T4)
knitr::kable(hsc3_T4_count) #テーブル化

hsc3_T4	n
2	2
3	2
4	9
5	21
6	29
7	43
NA	8

qq <- ggplot(data = data, mapping = aes(x = hsc3_T4, fill = factor(hsc3_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(qq) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc4_T4の度数分布とヒストグラム
hsc4_T4_count <- dplyr::count(data, hsc4_T4)
knitr::kable(hsc4_T4_count) #テーブル化

hsc4_T4	n
1	5
2	5
3	13
4	12
5	35
6	22
7	14
NA	8

rr <- ggplot(data = data, mapping = aes(x = hsc4_T4, fill = factor(hsc4_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(rr) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc5_T4の度数分布とヒストグラム
hsc5_T4_count <- dplyr::count(data, hsc5_T4)
knitr::kable(hsc5_T4_count) #テーブル化

hsc5_T4	n
2	1
3	4
4	3
5	19
6	23
7	54
NA	10

ss <- ggplot(data = data, mapping = aes(x = hsc5_T4, fill = factor(hsc5_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ss) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc6_T4の度数分布とヒストグラム
hsc6_T4_count <- dplyr::count(data, hsc6_T4)
knitr::kable(hsc6_T4_count) #テーブル化

hsc6_T4	n
1	2
2	8
3	8
4	9
5	38
6	29
7	12
NA	8

inu <- ggplot(data = data, mapping = aes(x = hsc6_T4, fill = factor(hsc6_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(inu) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc7_T4の度数分布とヒストグラム
hsc7_T4_count <- dplyr::count(data, hsc7_T4)
knitr::kable(hsc7_T4_count) #テーブル化

hsc7_T4	n
1	4
2	9
3	11
4	16
5	18
6	27
7	21
NA	8

tt <- ggplot(data = data, mapping = aes(x = hsc7_T4, fill = factor(hsc7_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(tt) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc8_T4の度数分布とヒストグラム
hsc8_T4_count <- dplyr::count(data, hsc8_T4)
knitr::kable(hsc8_T4_count) #テーブル化

hsc8_T4	n
1	2
2	5
3	17
4	20
5	34
6	17
7	11
NA	8

vv <- ggplot(data = data, mapping = aes(x = hsc8_T4, fill = factor(hsc8_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(vv) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc9_T4の度数分布とヒストグラム
hsc9_T4_count <- dplyr::count(data, hsc9_T4)
knitr::kable(hsc9_T4_count) #テーブル化

hsc9_T4	n
1	6
2	13
3	19
4	30
5	24
6	11
7	3
NA	8

ww <- ggplot(data = data, mapping = aes(x = hsc9_T4, fill = factor(hsc9_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ww) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc10_T4の度数分布とヒストグラム
hsc10_T4_count <- dplyr::count(data, hsc10_T4)
knitr::kable(hsc10_T4_count) #テーブル化

hsc10_T4	n
2	1
3	1
4	3
5	11
6	30
7	60
NA	8

kame <- ggplot(data = data, mapping = aes(x = hsc10_T4, fill = factor(hsc10_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kame) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc11_T4の度数分布とヒストグラム
hsc11_T4_count <- dplyr::count(data, hsc11_T4)
knitr::kable(hsc11_T4_count) #テーブル化

hsc11_T4	n
1	2
2	10
3	11
4	18
5	28
6	22
7	15
NA	8

yy <- ggplot(data = data, mapping = aes(x = hsc11_T4, fill = factor(hsc11_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(yy) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc12_T4の度数分布とヒストグラム
hsc12_T4_count <- dplyr::count(data, hsc12_T4)
knitr::kable(hsc12_T4_count) #テーブル化

hsc12_T4	n
1	1
2	5
3	2
4	7
5	31
6	31
7	29
NA	8

zz <- ggplot(data = data, mapping = aes(x = hsc12_T4, fill = factor(hsc12_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(zz) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb1_T1の度数分布とヒストグラム
wb1_T1_count <- dplyr::count(data, wb1_T1)
knitr::kable(wb1_T1_count) #テーブル化

wb1_T1	n
0	1
1	6
2	27
3	39
4	31
5	10

aaa <- ggplot(data = data, mapping = aes(x = wb1_T1, fill = factor(wb1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aaa) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb2_T1の度数分布とヒストグラム
wb2_T1_count <- dplyr::count(data, wb2_T1)
knitr::kable(wb2_T1_count) #テーブル化

wb2_T1	n
0	2
1	12
2	31
3	40
4	23
5	6

bbb <- ggplot(data = data, mapping = aes(x = wb2_T1, fill = factor(wb2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bbb) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb3_T1の度数分布とヒストグラム
wb3_T1_count <- dplyr::count(data, wb3_T1)
knitr::kable(wb3_T1_count) #テーブル化

wb3_T1	n
0	2
1	11
2	26
3	36
4	28
5	10
NA	1

ccc <- ggplot(data = data, mapping = aes(x = wb3_T1, fill = factor(wb3_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ccc) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb4_T1の度数分布とヒストグラム
wb4_T1_count <- dplyr::count(data, wb4_T1)
knitr::kable(wb4_T1_count) #テーブル化

wb4_T1	n
0	4
1	35
2	34
3	27
4	9
5	5

ddd <- ggplot(data = data, mapping = aes(x = wb4_T1, fill = factor(wb4_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ddd) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb5_T1の度数分布とヒストグラム
wb5_T1_count <- dplyr::count(data, wb5_T1)
knitr::kable(wb5_T1_count) #テーブル化

wb5_T1	n
0	3
1	15
2	29
3	38
4	22
5	7

eee <- ggplot(data = data, mapping = aes(x = wb5_T1, fill = factor(wb5_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(eee) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb1_T2の度数分布とヒストグラム
wb1_T2_count <- dplyr::count(data, wb1_T2)
knitr::kable(wb1_T2_count) #テーブル化

wb1_T2	n
1	5
2	23
3	30
4	33
5	9
NA	14

fff <- ggplot(data = data, mapping = aes(x = wb1_T2, fill = factor(wb1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(fff) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb2_T2の度数分布とヒストグラム
wb2_T2_count <- dplyr::count(data, wb2_T2)
knitr::kable(wb2_T2_count) #テーブル化

wb2_T2	n
1	7
2	23
3	28
4	32
5	9
NA	15

ggg <- ggplot(data = data, mapping = aes(x = wb2_T2, fill = factor(wb2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ggg) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb3_T2の度数分布とヒストグラム
wb3_T2_count <- dplyr::count(data, wb3_T2)
knitr::kable(wb3_T2_count) #テーブル化

wb3_T2	n
1	10
2	16
3	36
4	25
5	13
NA	14

hhh <- ggplot(data = data, mapping = aes(x = wb3_T2, fill = factor(wb3_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hhh) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb4_T2の度数分布とヒストグラム
wb4_T2_count <- dplyr::count(data, wb4_T2)
knitr::kable(wb4_T2_count) #テーブル化

wb4_T2	n
0	2
1	26
2	29
3	23
4	12
5	8
NA	14

iii <- ggplot(data = data, mapping = aes(x = wb4_T2, fill = factor(wb4_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(iii) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb5_T2の度数分布とヒストグラム
wb5_T2_count <- dplyr::count(data, wb5_T2)
knitr::kable(wb5_T2_count) #テーブル化

wb5_T2	n
1	12
2	26
3	28
4	21
5	13
NA	14

jjj <- ggplot(data = data, mapping = aes(x = wb5_T2, fill = factor(wb5_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jjj) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb1_T3の度数分布とヒストグラム
wb1_T3_count <- dplyr::count(data, wb1_T3)
knitr::kable(wb1_T3_count) #テーブル化

wb1_T3	n
0	1
1	6
2	18
3	36
4	28
5	16
NA	9

kkk <- ggplot(data = data, mapping = aes(x = wb1_T3, fill = factor(wb1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kkk) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb2_T3の度数分布とヒストグラム
wb2_T3_count <- dplyr::count(data, wb2_T3)
knitr::kable(wb2_T3_count) #テーブル化

wb2_T3	n
0	1
1	12
2	22
3	32
4	27
5	11
NA	9

lll <- ggplot(data = data, mapping = aes(x = wb2_T3, fill = factor(wb2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(lll) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb3_T3の度数分布とヒストグラム
wb3_T3_count <- dplyr::count(data, wb3_T3)
knitr::kable(wb1_T3_count) #テーブル化

wb1_T3	n
0	1
1	6
2	18
3	36
4	28
5	16
NA	9

mmm <- ggplot(data = data, mapping = aes(x = wb3_T3, fill = factor(wb3_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mmm) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb4_T3の度数分布とヒストグラム
wb4_T3_count <- dplyr::count(data, wb4_T3)
knitr::kable(wb4_T3_count) #テーブル化

wb4_T3	n
0	6
1	20
2	40
3	18
4	15
5	6
NA	9

nnn <- ggplot(data = data, mapping = aes(x = wb4_T3, fill = factor(wb4_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(nnn) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb5_T3の度数分布とヒストグラム
wb5_T3_count <- dplyr::count(data, wb5_T3)
knitr::kable(wb5_T3_count) #テーブル化

wb5_T3	n
0	1
1	16
2	27
3	38
4	16
5	7
NA	9

ooo <- ggplot(data = data, mapping = aes(x = wb5_T3, fill = factor(wb5_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ooo) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb1_T4の度数分布とヒストグラム
wb1_T4_count <- dplyr::count(data, wb1_T4)
knitr::kable(wb1_T4_count) #テーブル化

wb1_T4	n
0	1
1	10
2	17
3	34
4	34
5	10
NA	8

ppp <- ggplot(data = data, mapping = aes(x = wb1_T4, fill = factor(wb1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ppp) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb2_T4の度数分布とヒストグラム
wb2_T4_count <- dplyr::count(data, wb2_T4)
knitr::kable(wb2_T4_count) #テーブル化

wb2_T4	n
0	3
1	14
2	24
3	32
4	21
5	12
NA	8

qqq <- ggplot(data = data, mapping = aes(x = wb2_T4, fill = factor(wb2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(qqq) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb3_T4の度数分布とヒストグラム
wb3_T4_count <- dplyr::count(data, wb3_T4)
knitr::kable(wb3_T4_count) #テーブル化

wb3_T4	n
0	1
1	9
2	25
3	31
4	23
5	17
NA	8

rrr <- ggplot(data = data, mapping = aes(x = wb3_T4, fill = factor(wb3_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(rrr) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb5_T4の度数分布とヒストグラム
wb5_T4_count <- dplyr::count(data, wb5_T4)
knitr::kable(wb5_T4_count) #テーブル化

wb5_T4	n
0	4
1	12
2	25
3	30
4	20
5	15
NA	8

sss <- ggplot(data = data, mapping = aes(x = wb5_T4, fill = factor(wb5_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(sss) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#event1_T1の度数分布とヒストグラム
event1_T1_count <- dplyr::count(data, ev1_T1)
knitr::kable(event1_T1_count) #テーブル化

ev1_T1	n
-3	16
-2	11
-1	9
0	3
1	8
2	11
3	55
NA	1

ttt <- ggplot(data = data, mapping = aes(x = ev1_T1, fill = factor(ev1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ttt) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#event2_T1の度数分布とヒストグラム
event2_T1_count <- dplyr::count(data, ev2_T1)
knitr::kable(event2_T1_count) #テーブル化

ev2_T1	n
-3	21
-2	16
-1	8
0	5
1	2
2	19
3	42
NA	1

uuu <- ggplot(data = data, mapping = aes(x = ev2_T1, fill = factor(ev2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(uuu) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#event1_T2の度数分布とヒストグラム
event1_T2_count <- dplyr::count(data, ev1_T2)
knitr::kable(event1_T2_count) #テーブル化

ev1_T2	n
-3	17
-2	7
-1	4
0	4
1	6
2	13
3	47
NA	16

vvv <- ggplot(data = data, mapping = aes(x = ev1_T2, fill = factor(ev1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(vvv) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#event2_T2の度数分布とヒストグラム
event2_T2_count <- dplyr::count(data, ev2_T2)
knitr::kable(event2_T2_count) #テーブル化

ev2_T2	n
-3	20
-2	15
-1	5
0	1
1	8
2	17
3	29
NA	19

www <- ggplot(data = data, mapping = aes(x = ev2_T2, fill = factor(ev2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(www) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#event1_T3の度数分布とヒストグラム
event1_T3_count <- dplyr::count(data, ev1_T3)
knitr::kable(event1_T3_count) #テーブル化

ev1_T3	n
-3	19
-2	6
-1	10
0	6
1	4
2	11
3	47
NA	11

usagi <- ggplot(data = data, mapping = aes(x = ev1_T3, fill = factor(ev1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(usagi) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#event2_T3の度数分布とヒストグラム
event2_T3_count <- dplyr::count(data, ev2_T3)
knitr::kable(event2_T3_count) #テーブル化

ev2_T3	n
-3	22
-2	12
-1	4
0	5
1	13
2	13
3	34
NA	11

yyy <- ggplot(data = data, mapping = aes(x = ev2_T3, fill = factor(ev2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(yyy) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#event1_T4の度数分布とヒストグラム
event1_T4_count <- dplyr::count(data, ev1_T4)
knitr::kable(event1_T4_count) #テーブル化

ev1_T4	n
-3	15
-2	5
-1	3
0	4
1	10
2	15
3	49
NA	13

zzz <- ggplot(data = data, mapping = aes(x = ev1_T4, fill = factor(ev1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(zzz) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#event2_T4の度数分布とヒストグラム
event2_T4_count <- dplyr::count(data, ev2_T4)
knitr::kable(event2_T4_count) #テーブル化

ev2_T4	n
-3	15
-2	13
-1	10
0	9
1	9
2	14
3	31
NA	13

aaaa <- ggplot(data = data, mapping = aes(x = ev2_T4, fill = factor(ev2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aaaa) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#eoe_T1の度数分布とヒストグラム
eoe_T1_count <- dplyr::count(data, eoe_T1)
knitr::kable(eoe_T1_count) #テーブル化

eoe_T1	n
1.8	2
2.2	1
2.4	2
2.6	1
2.8	1
3.0	1
3.2	3
3.4	5
3.6	2
3.8	10
4.0	6
4.2	14
4.4	6
4.6	7
4.8	11
5.0	9
5.2	10
5.4	5
5.6	6
5.8	5
6.0	4
6.4	3

bbbb <- ggplot(data = data, mapping = aes(x = eoe_T1, fill = factor(eoe_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bbbb) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#eoe_T2の度数分布とヒストグラム
eoe_T2_count <- dplyr::count(data, eoe_T2)
knitr::kable(eoe_T2_count) #テーブル化

eoe_T2	n
1.8	2
2.0	1
2.4	1
2.6	2
2.8	2
3.0	1
3.2	2
3.4	2
3.6	2
3.8	4
4.0	5
4.2	8
4.4	6
4.6	9
4.8	16
5.0	3
5.2	7
5.4	5
5.6	9
5.8	2
6.0	3
6.2	4
6.4	1
6.6	1
6.8	1
NA	15

cccc <- ggplot(data = data, mapping = aes(x = eoe_T2, fill = factor(eoe_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(cccc) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#eoe_T3の度数分布とヒストグラム
eoe_T3_count <- dplyr::count(data, eoe_T3)
knitr::kable(eoe_T3_count) #テーブル化

eoe_T3	n
2.0	1
2.2	1
2.4	1
2.6	1
3.0	2
3.2	1
3.6	1
3.8	4
4.0	3
4.2	5
4.4	7
4.6	10
4.8	8
5.0	14
5.2	11
5.4	11
5.6	7
5.8	3
6.0	2
6.2	2
6.4	5
6.6	2
7.0	2
NA	10

dddd <- ggplot(data = data, mapping = aes(x = eoe_T3, fill = factor(eoe_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dddd) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#eoe_T4の度数分布とヒストグラム
eoe_T4_count <- dplyr::count(data, eoe_T4)
knitr::kable(eoe_T4_count) #テーブル化

eoe_T4	n
1.0	1
2.0	1
2.2	1
2.4	1
2.6	1
2.8	3
3.0	2
3.2	3
3.4	1
3.6	1
3.8	3
4.0	6
4.2	2
4.4	7
4.6	13
4.8	7
5.0	7
5.2	10
5.4	7
5.6	11
5.8	6
6.0	5
6.2	2
6.4	2
6.6	1
6.8	1
7.0	1
NA	8

eeee <- ggplot(data = data, mapping = aes(x = eoe_T4, fill = factor(eoe_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(eeee) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#lst_T1の度数分布とヒストグラム
lst_T1_count <- dplyr::count(data, lst_T1)
knitr::kable(lst_T1_count) #テーブル化

lst_T1	n
1.5	2
2.0	3
2.5	3
3.0	7
3.5	15
4.0	8
4.5	14
5.0	15
5.5	16
6.0	10
6.5	9
7.0	12

ffff <- ggplot(data = data, mapping = aes(x = lst_T1, fill = factor(lst_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ffff) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#lst_T2の度数分布とヒストグラム
lst_T2_count <- dplyr::count(data, lst_T2)
knitr::kable(lst_T2_count) #テーブル化

lst_T2	n
1.0	1
2.0	6
2.5	4
3.0	5
3.5	6
4.0	9
4.5	11
5.0	18
5.5	13
6.0	12
6.5	7
7.0	7
NA	15

gggg <- ggplot(data = data, mapping = aes(x = lst_T2, fill = factor(lst_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(gggg) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#lst_T3の度数分布とヒストグラム
lst_T3_count <- dplyr::count(data, lst_T3)
knitr::kable(lst_T3_count) #テーブル化

lst_T3	n
1.0	1
1.5	1
2.0	2
2.5	1
3.0	8
3.5	5
4.0	13
4.5	7
5.0	17
5.5	13
6.0	19
6.5	7
7.0	11
NA	9

hhhh <- ggplot(data = data, mapping = aes(x = lst_T3, fill = factor(lst_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hhhh) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#lst_T4の度数分布とヒストグラム
lst_T4_count <- dplyr::count(data, lst_T4)
knitr::kable(lst_T4_count) #テーブル化

lst_T4	n
2.0	6
2.5	3
3.0	9
3.5	4
4.0	10
4.5	8
5.0	22
5.5	11
6.0	18
6.5	6
7.0	9
NA	8

iiii <- ggplot(data = data, mapping = aes(x = lst_T4, fill = factor(lst_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(iiii) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#aes_T1の度数分布とヒストグラム
aes_T1_count <- dplyr::count(data, aes_T1)
knitr::kable(aes_T1_count) #テーブル化

aes_T1	n
3.25	1
4.25	1
4.50	2
4.75	6
5.00	6
5.25	15
5.50	9
5.75	22
6.00	21
6.25	11
6.50	9
6.75	5
7.00	5
NA	1

jjjj <- ggplot(data = data, mapping = aes(x = aes_T1, fill = factor(aes_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jjjj) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#aes_T2の度数分布とヒストグラム
aes_T2_count <- dplyr::count(data, aes_T2)
knitr::kable(aes_T2_count) #テーブル化

aes_T2	n
2.50	1
3.50	1
4.00	1
4.50	3
4.75	3
5.00	10
5.25	12
5.50	15
5.75	11
6.00	13
6.25	15
6.50	6
6.75	6
7.00	3
NA	14

kkkk <- ggplot(data = data, mapping = aes(x = aes_T2, fill = factor(aes_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kkkk) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#aes_T3の度数分布とヒストグラム
aes_T3_count <- dplyr::count(data, aes_T3)
knitr::kable(aes_T3_count) #テーブル化

aes_T3	n
4.00	2
4.25	1
4.50	2
4.75	1
5.00	10
5.25	9
5.50	13
5.75	15
6.00	9
6.25	17
6.50	14
6.75	7
7.00	5
NA	9

llll <- ggplot(data = data, mapping = aes(x = aes_T3, fill = factor(aes_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(llll) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#aes_T4の度数分布とヒストグラム
aes_T4_count <- dplyr::count(data, aes_T4)
knitr::kable(aes_T4_count) #テーブル化

aes_T4	n
2.75	2
3.50	1
4.25	2
4.50	2
4.75	4
5.00	5
5.25	10
5.50	12
5.75	10
6.00	16
6.25	13
6.50	12
6.75	14
7.00	1
NA	10

mmmm <- ggplot(data = data, mapping = aes(x = aes_T4, fill = factor(aes_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mmmm) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc_T1の度数分布とヒストグラム
hsc_T1_count <- dplyr::count(data, hsc_T1)
knitr::kable(hsc_T1_count) #テーブル化

hsc_T1	n
3.183333	1
3.300000	1
3.383333	1
3.400000	1
3.416667	1
3.650000	1
3.766667	1
3.933333	1
3.966667	1
3.983333	1
4.016667	1
4.066667	1
4.116667	1
4.150000	1
4.183333	1
4.266667	1
4.283333	1
4.316667	1
4.350000	2
4.383333	1
4.433333	1
4.450000	1
4.483333	3
4.500000	2
4.516667	1
4.633333	1
4.650000	1
4.683333	1
4.733333	1
4.750000	2
4.766667	2
4.800000	1
4.816667	1
4.850000	5
4.866667	1
4.883333	1
4.916667	1
4.933333	1
5.000000	2
5.016667	1
5.050000	1
5.066667	1
5.116667	1
5.150000	1
5.166667	2
5.200000	1
5.216667	2
5.233333	1
5.250000	1
5.283333	2
5.300000	1
5.316667	3
5.333333	2
5.366667	1
5.383333	1
5.416667	1
5.433333	2
5.450000	1
5.466667	1
5.483333	3
5.500000	1
5.516667	2
5.533333	1
5.566667	3
5.583333	1
5.616667	3
5.666667	1
5.683333	1
5.700000	1
5.733333	2
5.766667	1
5.800000	4
5.833333	1
5.850000	1
5.900000	1
5.916667	1
6.066667	1
6.083333	1
6.116667	1
6.133333	1
6.300000	1
6.383333	1
6.466667	1
6.600000	1
NA	1

nnnn <- ggplot(data = data, mapping = aes(x = hsc_T1, fill = factor(hsc_T1))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(nnnn) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc_T2の度数分布とヒストグラム
hsc_T2_count <- dplyr::count(data, hsc_T2)
knitr::kable(hsc_T2_count) #テーブル化

hsc_T2	n
2.533333	1
2.916667	1
3.066667	1
3.600000	1
3.616667	1
3.716667	1
3.850000	1
4.000000	1
4.016667	1
4.033333	1
4.050000	1
4.100000	2
4.166667	2
4.233333	1
4.316667	1
4.400000	1
4.433333	1
4.466667	1
4.500000	1
4.516667	3
4.533333	1
4.550000	2
4.566667	1
4.633333	1
4.650000	1
4.733333	2
4.766667	1
4.783333	2
4.866667	1
4.883333	2
4.900000	1
4.933333	2
4.950000	1
4.983333	1
5.000000	1
5.016667	1
5.066667	1
5.100000	1
5.116667	1
5.150000	2
5.183333	2
5.200000	2
5.233333	2
5.250000	1
5.283333	1
5.300000	1
5.333333	1
5.350000	1
5.383333	1
5.416667	2
5.433333	2
5.450000	1
5.466667	1
5.516667	1
5.533333	1
5.616667	2
5.633333	1
5.666667	2
5.683333	3
5.700000	3
5.766667	1
5.783333	1
5.800000	1
5.816667	1
5.866667	1
5.883333	1
5.950000	1
6.016667	2
6.066667	2
6.150000	1
6.233333	2
6.283333	1
6.683333	1
6.800000	1
NA	16

oooo <- ggplot(data = data, mapping = aes(x = hsc_T2, fill = factor(hsc_T2))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(oooo) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc_T3の度数分布とヒストグラム
hsc_T3_count <- dplyr::count(data, hsc_T3)
knitr::kable(hsc_T3_count) #テーブル化

hsc_T3	n
3.383333	1
3.483333	1
3.500000	1
3.766667	1
3.916667	1
3.950000	1
4.000000	1
4.016667	1
4.083333	1
4.150000	1
4.216667	1
4.283333	1
4.400000	1
4.433333	1
4.450000	1
4.483333	1
4.533333	1
4.566667	2
4.700000	2
4.766667	2
4.800000	1
4.866667	2
4.883333	1
4.916667	2
4.933333	1
4.950000	1
4.966667	2
4.983333	2
5.000000	1
5.033333	2
5.050000	1
5.100000	2
5.116667	1
5.133333	4
5.166667	2
5.183333	1
5.233333	1
5.250000	1
5.300000	1
5.316667	1
5.333333	2
5.400000	2
5.416667	4
5.433333	1
5.483333	2
5.500000	1
5.516667	1
5.533333	1
5.550000	1
5.583333	1
5.616667	1
5.633333	1
5.666667	1
5.683333	2
5.700000	1
5.716667	1
5.733333	2
5.800000	2
5.816667	1
5.883333	1
5.950000	1
5.983333	1
6.000000	1
6.016667	1
6.033333	1
6.050000	1
6.083333	1
6.100000	1
6.116667	1
6.133333	2
6.150000	1
6.166667	1
6.216667	1
6.333333	1
6.366667	1
6.383333	1
6.450000	2
6.566667	1
6.800000	1
6.916667	1
NA	10

pppp <- ggplot(data = data, mapping = aes(x = hsc_T3, fill = factor(hsc_T3))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(pppp) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc_T4の度数分布とヒストグラム
hsc_T4_count <- dplyr::count(data, hsc_T4)
knitr::kable(hsc_T4_count) #テーブル化

hsc_T4	n
2.616667	1
3.083333	1
3.183333	1
3.583333	1
3.633333	1
3.833333	1
3.850000	1
3.916667	1
3.933333	2
4.066667	1
4.233333	2
4.250000	1
4.333333	2
4.350000	1
4.366667	1
4.516667	1
4.533333	1
4.616667	3
4.633333	2
4.650000	1
4.666667	1
4.716667	1
4.750000	1
4.766667	1
4.783333	3
4.800000	1
4.833333	2
4.850000	1
4.866667	2
4.883333	1
4.900000	1
4.916667	1
4.933333	1
4.950000	1
5.000000	1
5.033333	1
5.100000	1
5.116667	2
5.150000	1
5.166667	1
5.200000	1
5.250000	1
5.300000	2
5.316667	2
5.333333	1
5.350000	1
5.366667	1
5.383333	1
5.400000	1
5.416667	1
5.433333	1
5.466667	1
5.483333	1
5.533333	2
5.550000	2
5.566667	1
5.633333	1
5.683333	2
5.700000	1
5.716667	1
5.733333	1
5.750000	2
5.766667	3
5.783333	2
5.816667	1
5.850000	1
5.866667	1
5.883333	1
5.900000	2
5.966667	1
5.983333	1
6.050000	1
6.116667	1
6.183333	1
6.250000	2
6.283333	1
6.416667	1
6.433333	1
6.450000	1
6.483333	1
6.550000	1
7.000000	1
NA	10

qqqq <- ggplot(data = data, mapping = aes(x = hsc_T4, fill = factor(hsc_T4))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(qqqq) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb_T1の度数分布とヒストグラム
wb_T1_count <- dplyr::count(data, wb_T1)
knitr::kable(wb_T1_count) #テーブル化

wb_T1	n
0.6	1
0.8	1
1.2	1
1.4	5
1.6	5
1.8	6
2.0	7
2.2	11
2.4	5
2.6	11
2.8	11
3.0	14
3.2	7
3.4	7
3.6	7
3.8	5
4.0	3
4.2	3
4.4	1
4.6	1
5.0	1
NA	1

rrrr <- ggplot(data = data, mapping = aes(x = wb_T1, fill = factor(wb_T1))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(rrrr) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb_T2の度数分布とヒストグラム
wb_T2_count <- dplyr::count(data, wb_T2)
knitr::kable(wb_T2_count) #テーブル化

wb_T2	n
1.0	1
1.2	1
1.4	1
1.6	5
1.8	6
2.0	4
2.2	7
2.4	7
2.6	8
2.8	2
3.0	15
3.2	10
3.4	7
3.6	2
3.8	7
4.0	4
4.2	2
4.4	5
4.6	2
5.0	3
NA	15

ssss <- ggplot(data = data, mapping = aes(x = wb_T2, fill = factor(wb_T2))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(ssss) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb_T3の度数分布とヒストグラム
wb_T3_count <- dplyr::count(data, wb_T3)
knitr::kable(wb_T3_count) #テーブル化

wb_T3	n
0.0	1
0.8	1
1.0	2
1.4	3
1.6	1
1.8	5
2.0	5
2.2	8
2.4	10
2.6	7
2.8	10
3.0	12
3.2	8
3.4	5
3.6	4
3.8	10
4.0	5
4.4	1
4.6	3
4.8	1
5.0	3
NA	9

tttt <- ggplot(data = data, mapping = aes(x = wb_T3, fill = factor(wb_T3))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(tttt) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb_T4の度数分布とヒストグラム
wb_T4_count <- dplyr::count(data, wb_T4)
knitr::kable(wb_T4_count) #テーブル化

wb_T4	n
0.2	1
0.6	1
0.8	2
1.0	2
1.2	3
1.4	1
1.6	2
1.8	4
2.0	8
2.2	8
2.4	6
2.6	13
2.8	8
3.0	3
3.2	10
3.4	7
3.6	4
3.8	3
4.0	5
4.2	5
4.4	2
4.6	2
5.0	6
NA	8

uuuu <- ggplot(data = data, mapping = aes(x = wb_T4, fill = factor(wb_T4))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(uuuu) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#ev_T1の度数分布とヒストグラム
ev_T1_count <- dplyr::count(data, ev_T1)
knitr::kable(ev_T1_count) #テーブル化

ev_T1	n
-3.0	3
-2.5	1
-2.0	1
-1.5	7
-1.0	4
-0.5	9
0.0	21
0.5	18
1.0	11
1.5	1
2.0	3
2.5	15
3.0	19
NA	1

vvvv <- ggplot(data = data, mapping = aes(x = ev_T1, fill = factor(ev_T1))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(vvvv) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#ev_T2の度数分布とヒストグラム
ev_T2_count <- dplyr::count(data, ev_T2)
knitr::kable(ev_T2_count) #テーブル化

ev_T2	n
-3.0	3
-2.5	5
-2.0	1
-1.5	3
-1.0	4
-0.5	3
0.0	22
0.5	10
1.0	9
1.5	3
2.0	6
2.5	12
3.0	14
NA	19

wwww <- ggplot(data = data, mapping = aes(x = ev_T2, fill = factor(ev_T2))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(wwww) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#ev_T3の度数分布とヒストグラム
ev_T3_count <- dplyr::count(data, ev_T3)
knitr::kable(ev_T3_count) #テーブル化

ev_T3	n
-3.0	7
-2.0	3
-1.5	1
-1.0	3
-0.5	8
0.0	20
0.5	17
1.0	8
1.5	6
2.0	10
2.5	5
3.0	15
NA	11

panda <- ggplot(data = data, mapping = aes(x = ev_T3, fill = factor(ev_T3))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(panda) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#ev_T4の度数分布とヒストグラム
ev_T4_count <- dplyr::count(data, ev_T4)
knitr::kable(ev_T4_count) #テーブル化

ev_T4	n
-3.0	3
-2.0	1
-1.0	3
-0.5	10
0.0	21
0.5	15
1.0	9
1.5	8
2.0	10
2.5	7
3.0	14
NA	13

yyyy <- ggplot(data = data, mapping = aes(x = ev_T4, fill = factor(ev_T4))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(yyyy) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#hsc_onemonthの度数分布とヒストグラム
hsc_onemonth_count <- dplyr::count(data, hsc_onemonth)
knitr::kable(hsc_onemonth_count) #テーブル化

hsc_onemonth	n
3.170833	1
3.533333	1
3.750000	1
3.887500	1
4.033333	1
4.100000	1
4.145833	1
4.262500	1
4.270833	1
4.279167	1
4.437500	1
4.441667	1
4.475000	1
4.504167	1
4.575000	2
4.600000	1
4.604167	1
4.645833	1
4.679167	1
4.750000	1
4.754167	1
4.762500	1
4.775000	1
4.804167	1
4.837500	1
4.883333	1
4.904167	1
4.912500	1
4.962500	1
4.966667	1
4.975000	1
5.008333	1
5.016667	1
5.037500	1
5.041667	1
5.058333	1
5.066667	1
5.079167	1
5.079167	1
5.116667	1
5.175000	1
5.200000	1
5.241667	1
5.270833	1
5.275000	1
5.287500	1
5.304167	2
5.329167	1
5.362500	1
5.383333	1
5.391667	1
5.400000	1
5.404167	1
5.420833	1
5.425000	1
5.533333	1
5.537500	1
5.554167	1
5.591667	1
5.608333	1
5.625000	1
5.637500	2
5.641667	1
5.650000	1
5.729167	1
5.737500	1
5.783333	1
5.795833	1
5.812500	1
5.829167	1
5.875000	1
5.879167	1
5.900000	1
5.987500	1
6.012500	1
6.029167	1
6.054167	1
6.095833	1
6.158333	1
6.237500	1
6.258333	1
6.570833	1
6.687500	1
NA	28

zzzz <- ggplot(data = data, mapping = aes(x = hsc_onemonth, fill = factor(hsc_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(zzzz) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#wb_onemonthの度数分布とヒストグラム
wb_onemonth_count <- dplyr::count(data, wb_onemonth)
knitr::kable(wb_onemonth_count) #テーブル化

wb_onemonth	n
1.15	1
1.25	1
1.30	1
1.40	1
1.65	1
1.75	1
1.80	1
1.85	1
1.95	1
2.00	1
2.00	1
2.10	2
2.15	1
2.20	1
2.25	2
2.30	1
2.30	1
2.35	1
2.40	3
2.45	1
2.45	2
2.50	3
2.55	1
2.60	1
2.70	1
2.75	1
2.75	1
2.80	4
2.80	1
2.85	4
2.85	2
2.85	2
2.90	1
2.90	1
2.95	2
2.95	3
3.05	4
3.05	1
3.15	1
3.20	1
3.30	1
3.35	2
3.40	1
3.45	1
3.45	1
3.50	1
3.55	1
3.60	2
3.60	1
3.65	1
3.75	1
3.80	1
3.80	1
3.85	1
4.05	1
4.10	1
4.20	2
4.25	2
4.40	1
4.45	2
4.55	1
4.85	1
NA	26

A <- ggplot(data = data, mapping = aes(x = wb_onemonth, fill = factor(wb_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(A) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

#ev_onemonthの度数分布とヒストグラム
ev_onemonth_count <- dplyr::count(data, ev_onemonth)
knitr::kable(ev_onemonth_count) #テーブル化

ev_onemonth	n
-1.625	1
-1.125	1
-0.875	1
-0.750	2
-0.625	1
-0.500	1
-0.375	1
-0.250	3
-0.125	2
0.000	4
0.125	6
0.250	7
0.375	2
0.500	4
0.625	2
0.750	6
0.875	3
1.000	2
1.125	2
1.250	3
1.375	5
1.500	5
1.625	3
1.750	4
1.875	3
2.125	2
2.250	2
2.500	1
2.625	2
2.750	1
2.875	2
NA	30

B <- ggplot(data = data, mapping = aes(x = ev_onemonth, fill = factor(ev_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(B) #視覚化

## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

1-4. 記述統計量

#hsc_T1
hsc_T1_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   hsc1.T1.mean = mean (hsc1_T1), #hsc1_T1の平均
                   hsc1.T1.sd = sd (hsc1_T1), #hsc1_T1のSD
                   hsc2.T1.mean = mean (hsc2_T1), 
                   hsc2.T1.sd = sd (hsc2_T1),
                   hsc3.T1.mean = mean (hsc3_T1), 
                   hsc3.T1.sd = sd (hsc3_T1),
                   hsc4.T1.mean = mean (hsc4_T1), 
                   hsc4.T1.sd = sd (hsc4_T1),
                   hsc5.T1.mean = mean (hsc5_T1), 
                   hsc5.T1.sd = sd (hsc5_T1),
                   hsc6.T1.mean = mean (hsc6_T1), 
                   hsc6.T1.sd = sd (hsc6_T1),
                   hsc7.T1.mean = mean (hsc7_T1), 
                   hsc7.T1.sd = sd (hsc7_T1),
                   hsc8.T1.mean = mean (hsc8_T1), 
                   hsc8.T1.sd = sd (hsc8_T1),
                   hsc9.T1.mean = mean (hsc9_T1), 
                   hsc9.T1.sd = sd (hsc9_T1),
                   hsc10.T1.mean = mean (hsc10_T1), 
                   hsc10.T1.sd = sd (hsc10_T1),
                   hsc11.T1.mean = mean (hsc11_T1), 
                   hsc11.T1.sd = sd (hsc11_T1),
                   hsc12.T1.mean = mean (hsc4_T1), 
                   hsc12.T1.sd = sd (hsc4_T1),
                   eoe.mean.T1 = mean (eoe_T1),
                   eoe.sd.T1 = sd (eoe_T1),
                   lst.mean.T1 = mean (lst_T1),
                   lst.sd.T1 = sd (lst_T1),
                   aes.mean.T1 = mean (aes_T1),
                   aes.sd.T1 = sd (aes_T1),
                   hsc.mean.T1 = mean (hsc_T1),
                   hsc.sd.T1 = sd (hsc_T1))
knitr::kable(hsc_T1_discriptive, digits = 2) #出力

n	hsc1.T1.mean	hsc1.T1.sd	hsc2.T1.mean	hsc2.T1.sd	hsc3.T1.mean	hsc3.T1.sd	hsc4.T1.mean	hsc4.T1.sd	hsc5.T1.mean	hsc5.T1.sd	hsc6.T1.mean	hsc6.T1.sd	hsc7.T1.mean	hsc7.T1.sd	hsc8.T1.mean	hsc8.T1.sd	hsc9.T1.mean	hsc9.T1.sd	hsc10.T1.mean	hsc10.T1.sd	hsc11.T1.mean	hsc11.T1.sd	hsc12.T1.mean	hsc12.T1.sd	eoe.mean.T1	eoe.sd.T1	lst.mean.T1	lst.sd.T1	aes.mean.T1	aes.sd.T1	hsc.mean.T1	hsc.sd.T1
79	4.68	1.38	4.75	1.55	5.7	1.22	4.52	1.7	6.25	0.85	4.95	1.35	4.68	1.82	4.15	1.46	3.59	1.42	6.52	0.78	4.97	1.52	4.52	1.7	4.58	0.95	4.86	1.39	5.79	0.6	5.08	0.73

#hsc_T2
hsc_T2_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   hsc1.T2.mean = mean (hsc1_T2), #hsc1_T2の平均
                   hsc1.T2.sd = sd (hsc1_T2), #hsc1_T2のSD
                   hsc2.T2.mean = mean (hsc2_T2), 
                   hsc2.T2.sd = sd (hsc2_T2),
                   hsc3.T2.mean = mean (hsc3_T2), 
                   hsc3.T2.sd = sd (hsc3_T2),
                   hsc4.T2.mean = mean (hsc4_T2), 
                   hsc4.T2.sd = sd (hsc4_T2),
                   hsc5.T2.mean = mean (hsc5_T2), 
                   hsc5.T2.sd = sd (hsc5_T2),
                   hsc6.T2.mean = mean (hsc6_T2), 
                   hsc6.T2.sd = sd (hsc6_T2),
                   hsc7.T2.mean = mean (hsc7_T2), 
                   hsc7.T2.sd = sd (hsc7_T2),
                   hsc8.T2.mean = mean (hsc8_T2), 
                   hsc8.T2.sd = sd (hsc8_T2),
                   hsc9.T2.mean = mean (hsc9_T2), 
                   hsc9.T2.sd = sd (hsc9_T2),
                   hsc10.T2.mean = mean (hsc10_T2), 
                   hsc10.T2.sd = sd (hsc10_T2),
                   hsc11.T2.mean = mean (hsc11_T2), 
                   hsc11.T2.sd = sd (hsc11_T2),
                   hsc12.T2.mean = mean (hsc4_T2), 
                   hsc12.T2.sd = sd (hsc4_T2),
                   eoe.mean.T2 = mean (eoe_T2),
                   eoe.sd.T2 = sd (eoe_T2),
                   lst.mean.T2 = mean (lst_T2),
                   lst.sd.T2 = sd (lst_T2),
                   aes.mean.T2 = mean (aes_T2),
                   aes.sd.T2 = sd (aes_T2),
                   hsc.mean.T2 = mean (hsc_T2),
                   hsc.sd.T2 = sd (hsc_T2)) 
knitr::kable(hsc_T2_discriptive, digits = 2) #出力

n	hsc1.T2.mean	hsc1.T2.sd	hsc2.T2.mean	hsc2.T2.sd	hsc3.T2.mean	hsc3.T2.sd	hsc4.T2.mean	hsc4.T2.sd	hsc5.T2.mean	hsc5.T2.sd	hsc6.T2.mean	hsc6.T2.sd	hsc7.T2.mean	hsc7.T2.sd	hsc8.T2.mean	hsc8.T2.sd	hsc9.T2.mean	hsc9.T2.sd	hsc10.T2.mean	hsc10.T2.sd	hsc11.T2.mean	hsc11.T2.sd	hsc12.T2.mean	hsc12.T2.sd	eoe.mean.T2	eoe.sd.T2	lst.mean.T2	lst.sd.T2	aes.mean.T2	aes.sd.T2	hsc.mean.T2	hsc.sd.T2
79	4.73	1.17	4.92	1.44	5.73	1.17	4.71	1.63	6.18	0.98	5.06	1.24	4.81	1.71	4.46	1.29	3.8	1.4	6.42	0.93	4.92	1.53	4.71	1.63	4.75	0.94	4.92	1.39	5.77	0.65	5.15	0.76

#hsc_T3
hsc_T3_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   hsc1.T3.mean = mean (hsc1_T3), #hsc1_T3の平均
                   hsc1.T3.sd = sd (hsc1_T3), #hsc1_T3のSD
                   hsc2.T3.mean = mean (hsc2_T3), 
                   hsc2.T3.sd = sd (hsc2_T3),
                   hsc3.T3.mean = mean (hsc3_T3), 
                   hsc3.T3.sd = sd (hsc3_T3),
                   hsc4.T3.mean = mean (hsc4_T3), 
                   hsc4.T3.sd = sd (hsc4_T3),
                   hsc5.T3.mean = mean (hsc5_T3), 
                   hsc5.T3.sd = sd (hsc5_T3),
                   hsc6.T3.mean = mean (hsc6_T3), 
                   hsc6.T3.sd = sd (hsc6_T3),
                   hsc7.T3.mean = mean (hsc7_T3), 
                   hsc7.T3.sd = sd (hsc7_T3),
                   hsc8.T3.mean = mean (hsc8_T3), 
                   hsc8.T3.sd = sd (hsc8_T3),
                   hsc9.T3.mean = mean (hsc9_T3), 
                   hsc9.T3.sd = sd (hsc9_T3),
                   hsc10.T3.mean = mean (hsc10_T3), 
                   hsc10.T3.sd = sd (hsc10_T3),
                   hsc11.T3.mean = mean (hsc11_T3), 
                   hsc11.T3.sd = sd (hsc11_T3),
                   hsc12.T3.mean = mean (hsc4_T3), 
                   hsc12.T3.sd = sd (hsc4_T3),
                   eoe.mean.T3 = mean (eoe_T3),
                   eoe.sd.T3 = sd (eoe_T3),
                   lst.mean.T3 = mean (lst_T3),
                   lst.sd.T3 = sd (lst_T3),
                   aes.mean.T3 = mean (aes_T3),
                   aes.sd.T3 = sd (aes_T3),
                   hsc.mean.T3 = mean (hsc_T3),
                   hsc.sd.T3 = sd (hsc_T3)) 
knitr::kable(hsc_T3_discriptive, digits = 2) #出力

n	hsc1.T3.mean	hsc1.T3.sd	hsc2.T3.mean	hsc2.T3.sd	hsc3.T3.mean	hsc3.T3.sd	hsc4.T3.mean	hsc4.T3.sd	hsc5.T3.mean	hsc5.T3.sd	hsc6.T3.mean	hsc6.T3.sd	hsc7.T3.mean	hsc7.T3.sd	hsc8.T3.mean	hsc8.T3.sd	hsc9.T3.mean	hsc9.T3.sd	hsc10.T3.mean	hsc10.T3.sd	hsc11.T3.mean	hsc11.T3.sd	hsc12.T3.mean	hsc12.T3.sd	eoe.mean.T3	eoe.sd.T3	lst.mean.T3	lst.sd.T3	aes.mean.T3	aes.sd.T3	hsc.mean.T3	hsc.sd.T3
79	4.78	1.17	5.08	1.54	6.06	1.1	4.97	1.49	6.32	1.01	5.32	1.39	4.63	1.88	4.67	1.49	3.87	1.5	6.57	0.69	4.86	1.55	4.97	1.49	4.91	1.01	4.97	1.43	5.93	0.61	5.27	0.77

#hsc_T4
hsc_T4_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   hsc1.T4.mean = mean (hsc1_T4), #hsc1_T4の平均
                   hsc1.T4.sd = sd (hsc1_T4), #hsc1_T4のSD
                   hsc2.T4.mean = mean (hsc2_T4), 
                   hsc2.T4.sd = sd (hsc2_T4),
                   hsc3.T4.mean = mean (hsc3_T4), 
                   hsc3.T4.sd = sd (hsc3_T4),
                   hsc4.T4.mean = mean (hsc4_T4), 
                   hsc4.T4.sd = sd (hsc4_T4),
                   hsc5.T4.mean = mean (hsc5_T4), 
                   hsc5.T4.sd = sd (hsc5_T4),
                   hsc6.T4.mean = mean (hsc6_T4), 
                   hsc6.T4.sd = sd (hsc6_T4),
                   hsc7.T4.mean = mean (hsc7_T4), 
                   hsc7.T4.sd = sd (hsc7_T4),
                   hsc8.T4.mean = mean (hsc8_T4), 
                   hsc8.T4.sd = sd (hsc8_T4),
                   hsc9.T4.mean = mean (hsc9_T4), 
                   hsc9.T4.sd = sd (hsc9_T4),
                   hsc10.T4.mean = mean (hsc10_T4), 
                   hsc10.T4.sd = sd (hsc10_T4),
                   hsc11.T4.mean = mean (hsc11_T4), 
                   hsc11.T4.sd = sd (hsc11_T4),
                   hsc12.T4.mean = mean (hsc4_T4), 
                   hsc12.T4.sd = sd (hsc4_T4),
                   eoe.mean.T4 = mean (eoe_T4),
                   eoe.sd.T4 = sd (eoe_T4),
                   lst.mean.T4 = mean (lst_T4),
                   lst.sd.T4 = sd (lst_T4),
                   aes.mean.T4 = mean (aes_T4),
                   aes.sd.T4 = sd (aes_T4),
                   hsc.mean.T4 = mean (hsc_T4),
                   hsc.sd.T4 = sd (hsc_T4)) 
knitr::kable(hsc_T4_discriptive, digits = 2) #出力

n	hsc1.T4.mean	hsc1.T4.sd	hsc2.T4.mean	hsc2.T4.sd	hsc3.T4.mean	hsc3.T4.sd	hsc4.T4.mean	hsc4.T4.sd	hsc5.T4.mean	hsc5.T4.sd	hsc6.T4.mean	hsc6.T4.sd	hsc7.T4.mean	hsc7.T4.sd	hsc8.T4.mean	hsc8.T4.sd	hsc9.T4.mean	hsc9.T4.sd	hsc10.T4.mean	hsc10.T4.sd	hsc11.T4.mean	hsc11.T4.sd	hsc12.T4.mean	hsc12.T4.sd	eoe.mean.T4	eoe.sd.T4	lst.mean.T4	lst.sd.T4	aes.mean.T4	aes.sd.T4	hsc.mean.T4	hsc.sd.T4
79	4.82	1.15	5.05	1.5	6.11	1.07	4.86	1.65	6.24	1.06	5.09	1.37	4.8	1.86	4.71	1.43	3.92	1.48	6.59	0.69	4.82	1.63	4.86	1.65	4.85	1.11	4.94	1.45	5.94	0.67	5.24	0.78

#hsc_onemonth
hsc_onemonth_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   hsc1.onemonth.mean = mean (hsc_onemonth), #hsc_onemonthの平均
                   hsc1.onemonth.sd = sd (hsc_onemonth)) #hsc_onemonthのSD
knitr::kable(hsc_onemonth_discriptive, digits = 2) #出力

n	hsc1.onemonth.mean	hsc1.onemonth.sd
79	5.18	0.68

#wb_T1
wb_T1_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   wb1.T1.mean = mean (wb1_T1), #hsc1_T10の平均
                   wb1.T1.sd = sd (wb1_T1), #hsc1_T10のSD
                   wb2.T1.mean = mean (wb2_T1), 
                   wb2.T1.sd = sd (wb2_T1),
                   wb3.T1.mean = mean (wb3_T1), 
                   wb3.T1.sd = sd (wb3_T1),
                   wb4.T1.mean = mean (wb4_T1), 
                   wb4.T1.sd = sd (wb4_T1),
                   wb5.T1.mean = mean (wb5_T1), 
                   wb5.T1.sd = sd (wb5_T1),
                   wb.T1.mean = mean (wb_T1),
                   wb.T1.sd = sd (wb_T1))
knitr::kable(wb_T1_discriptive, digits = 2) #出力

n	wb1.T1.mean	wb1.T1.sd	wb2.T1.mean	wb2.T1.sd	wb3.T1.mean	wb3.T1.sd	wb4.T1.mean	wb4.T1.sd	wb5.T1.mean	wb5.T1.sd	wb.T1.mean	wb.T1.sd
79	3.22	1	2.87	1.14	3.04	1.11	2.23	1.17	2.86	1.14	2.84	0.8

#wb_T2
wb_T2_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   wb1.T2.mean = mean (wb1_T2), #hsc1_T2の平均
                   wb1.T2.sd = sd (wb1_T2), #hsc1_T2のSD
                   wb2.T2.mean = mean (wb2_T2), 
                   wb2.T2.sd = sd (wb2_T2),
                   wb3.T2.mean = mean (wb3_T2), 
                   wb3.T2.sd = sd (wb3_T2),
                   wb4.T2.mean = mean (wb4_T2), 
                   wb4.T2.sd = sd (wb4_T2),
                   wb5.T2.mean = mean (wb5_T2), 
                   wb5.T2.sd = sd (wb5_T2),
                   wb.T2.mean = mean (wb_T2),
                   wb.T2.sd = sd (wb_T2))
knitr::kable(wb_T2_discriptive, digits = 2) #出力

n	wb1.T2.mean	wb1.T2.sd	wb2.T2.mean	wb2.T2.sd	wb3.T2.mean	wb3.T2.sd	wb4.T2.mean	wb4.T2.sd	wb5.T2.mean	wb5.T2.sd	wb.T2.mean	wb.T2.sd
79	3.22	1	3.13	1.11	3.15	1.17	2.38	1.29	2.94	1.24	2.96	0.93

#wb_T3
wb_T3_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   wb1.T3.mean = mean (wb1_T3), #hsc1_T3の平均
                   wb1.T3.sd = sd (wb1_T3), #hsc1_T3のSD
                   wb2.T3.mean = mean (wb2_T3), 
                   wb2.T3.sd = sd (wb2_T3),
                   wb3.T3.mean = mean (wb3_T3), 
                   wb3.T3.sd = sd (wb3_T3),
                   wb4.T3.mean = mean (wb4_T3), 
                   wb4.T3.sd = sd (wb4_T3),
                   wb5.T3.mean = mean (wb5_T3), 
                   wb5.T3.sd = sd (wb5_T3),
                   wb.T3.mean = mean (wb_T3),
                   wb.T3.sd = sd (wb_T3))
knitr::kable(wb_T3_discriptive, digits = 2) #出力

n	wb1.T3.mean	wb1.T3.sd	wb2.T3.mean	wb2.T3.sd	wb3.T3.mean	wb3.T3.sd	wb4.T3.mean	wb4.T3.sd	wb5.T3.mean	wb5.T3.sd	wb.T3.mean	wb.T3.sd
79	3.35	1.16	3.04	1.24	3.28	1.12	2.32	1.25	2.68	1.16	2.93	0.92

#wb_T4
wb_T4_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   wb1.T4.mean = mean (wb1_T4), #hsc1_T4の平均
                   wb1.T4.sd = sd (wb1_T4), #hsc1_T4のSD
                   wb2.T4.mean = mean (wb2_T4), 
                   wb2.T4.sd = sd (wb2_T4),
                   wb3.T4.mean = mean (wb3_T4), 
                   wb3.T4.sd = sd (wb3_T4),
                   wb4.T4.mean = mean (wb4_T4), 
                   wb4.T4.sd = sd (wb4_T4),
                   wb5.T4.mean = mean (wb5_T4), 
                   wb5.T4.sd = sd (wb5_T4),
                   wb.T4.mean = mean (wb_T4),
                   wb.T4.sd = sd (wb_T4))
knitr::kable(wb_T4_discriptive, digits = 2) #出力

n	wb1.T4.mean	wb1.T4.sd	wb2.T4.mean	wb2.T4.sd	wb3.T4.mean	wb3.T4.sd	wb4.T4.mean	wb4.T4.sd	wb5.T4.mean	wb5.T4.sd	wb.T4.mean	wb.T4.sd
79	3.18	1.11	2.91	1.21	3.15	1.22	2.33	1.32	2.89	1.37	2.89	1.03

#wb_onemonth
wb_onemonth_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   wb.onemonth.mean = mean (wb_onemonth), #wb_onemonthの平均
                   wb.onemonth.sd = sd (wb_onemonth)) #wb_onemonthのSD
knitr::kable(wb_onemonth_discriptive, digits = 2) #出力

n	wb.onemonth.mean	wb.onemonth.sd
79	2.91	0.81

#ev_T1
ev_T1_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   ev1.T1.mean = mean (ev1_T1), #ev1_T1の平均
                   ev1.T1.sd = sd (ev1_T1), #ev1_T1のSD
                   ev2.T1.mean = mean (ev2_T1), 
                   ev2.T1.sd = sd (ev2_T1),
                   ev.T1.mean = mean (ev_T1),
                   ev.T1.sd = sd (ev_T1))
knitr::kable(ev_T1_discriptive, digits = 2) #出力

n	ev1.T1.mean	ev1.T1.sd	ev2.T1.mean	ev2.T1.sd	ev.T1.mean	ev.T1.sd
79	1.03	2.36	1.03	2.42	1.03	1.6

#ev_T2
ev_T2_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   ev1.T2.mean = mean (ev1_T2), #ev1_T2の平均
                   ev1.T2.sd = sd (ev1_T2), #ev1_T2のSD
                   ev2.T2.mean = mean (ev2_T2), 
                   ev2.T2.sd = sd (ev2_T2),
                   ev.T2.mean = mean (ev_T2),
                   ev.T2.sd = sd (ev_T2))
knitr::kable(ev_T2_discriptive, digits = 2) #出力

n	ev1.T2.mean	ev1.T2.sd	ev2.T2.mean	ev2.T2.sd	ev.T2.mean	ev.T2.sd
79	1.29	2.27	0.2	2.48	0.75	1.62

#ev_T3
ev_T3_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   ev1.T3.mean = mean (ev_T3), #ev1_T3の平均
                   ev1.T3.sd = sd (ev1_T3), #ev1_T3のSD
                   ev2.T3.mean = mean (ev2_T3), 
                   ev2.T3.sd = sd (ev2_T3),
                   ev.T3.mean = mean (ev_T3),
                   ev.T3.sd = sd (ev_T3))
knitr::kable(ev_T3_discriptive, digits = 2) #出力

n	ev1.T3.mean	ev1.T3.sd	ev2.T3.mean	ev2.T3.sd	ev.T3.mean	ev.T3.sd
79	0.73	2.38	0.53	2.43	0.73	1.58

#ev_T4
ev_T4_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   ev1.T4.mean = mean (ev1_T4), #ev1_T4の平均
                   ev1.T4.sd = sd (ev1_T4), #ev1_T4のSD
                   ev2.T4.mean = mean (ev2_T4), 
                   ev2.T4.sd = sd (ev2_T4),
                   ev.T4.mean = mean (ev_T4),
                   ev.T4.sd = sd (ev_T4))
knitr::kable(ev_T4_discriptive, digits = 2) #出力

n	ev1.T4.mean	ev1.T4.sd	ev2.T4.mean	ev2.T4.sd	ev.T4.mean	ev.T4.sd
79	1.33	2.27	0.52	2.3	0.92	1.47

#ev_onemonth
ev_onemonth_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   ev.onemonth.mean = mean (ev_onemonth), #ev_onemonthの平均
                   ev.onemonth.sd = sd (ev_onemonth)) #ev_onemonthのSD
knitr::kable(ev_onemonth_discriptive, digits = 2) #出力

n	ev.onemonth.mean	ev.onemonth.sd
79	0.86	0.98

#age_T1
age_T1_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   age.T1.mean = mean (age_T1), #age_T1の平均
                   age.T1.sd = sd (age_T1)) #age_T1のSD
knitr::kable(age_T1_discriptive, digits = 2) #出力

n	age.T1.mean	age.T1.sd
79	18.65	0.77

#age_T2
age_T2_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   age.T2.mean = mean (age_T2), #age_T2の平均
                   age.T2.sd = sd (age_T2)) #age_T2のSD
knitr::kable(age_T2_discriptive, digits = 2) #出力

n	age.T2.mean	age.T2.sd
79	18.65	0.77

#age_T3
age_T3_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   age.T3.mean = mean (age_T3), #age_T3の平均
                   age.T3.sd = sd (age_T3)) #age_T3のSD
knitr::kable(age_T3_discriptive, digits = 2) #出力

n	age.T3.mean	age.T3.sd
79	18.68	0.76

#age_T4
age_T4_discriptive <- 
  data %>%
  drop_na() %>%
  dplyr::summarise(n = n (), #グループの人数を出力
                   age.T4.mean = mean (age_T4), #age_T4の平均
                   age.T4.sd = sd (age_T4)) #age_T4のSD
knitr::kable(age_T4_discriptive, digits = 2) #出力

n	age.T4.mean	age.T4.sd
79	18.68	0.76

1-5. 内的整合性

library(psych)

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

library(GPArotation)

#hsc_T1
alpha(data[, c(6,7,8,9,11,12,13,14,15,16,17)]) #alpha .63

## 
## Reliability analysis   
## Call: alpha(x = data[, c(6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.64      0.63    0.69      0.14 1.7 0.048  4.9 0.68     0.13
## 
##  lower alpha upper     95% confidence boundaries
## 0.55 0.64 0.74 
## 
##  Reliability if an item is dropped:
##          raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T1       0.66      0.65    0.69      0.16 1.9    0.047 0.024 0.129
## hsc2_T1       0.58      0.57    0.61      0.12 1.3    0.057 0.020 0.094
## hsc3_T1       0.64      0.64    0.69      0.15 1.8    0.048 0.028 0.140
## hsc4_T1       0.59      0.58    0.65      0.12 1.4    0.056 0.027 0.094
## hsc6_T1       0.59      0.58    0.63      0.12 1.4    0.056 0.021 0.105
## hsc7_T1       0.67      0.65    0.70      0.16 1.9    0.044 0.025 0.145
## hsc8_T1       0.56      0.55    0.61      0.11 1.2    0.059 0.019 0.094
## hsc9_T1       0.62      0.62    0.68      0.14 1.6    0.051 0.026 0.129
## hsc10_T1      0.64      0.65    0.69      0.15 1.8    0.049 0.026 0.140
## hsc11_T1      0.59      0.59    0.63      0.12 1.4    0.055 0.022 0.105
## hsc12_T1      0.62      0.61    0.67      0.14 1.6    0.051 0.025 0.105
## 
##  Item statistics 
##            n raw.r std.r r.cor r.drop mean   sd
## hsc1_T1  114  0.27  0.28 0.142  0.087  4.8 1.38
## hsc2_T1  114  0.63  0.63 0.643  0.467  4.7 1.62
## hsc3_T1  113  0.31  0.34 0.193  0.139  5.6 1.31
## hsc4_T1  114  0.60  0.57 0.506  0.416  4.4 1.78
## hsc6_T1  114  0.58  0.59 0.561  0.437  4.9 1.41
## hsc7_T1  114  0.29  0.26 0.095  0.063  4.8 1.74
## hsc8_T1  114  0.70  0.68 0.700  0.581  4.1 1.38
## hsc9_T1  114  0.44  0.43 0.317  0.264  3.6 1.43
## hsc10_T1 114  0.21  0.30 0.152  0.111  6.5 0.77
## hsc11_T1 114  0.58  0.57 0.551  0.410  4.9 1.51
## hsc12_T1 114  0.46  0.46 0.360  0.286  5.6 1.43
## 
## Non missing response frequency for each item
##             1    2    3    4    5    6    7 miss
## hsc1_T1  0.02 0.07 0.09 0.14 0.42 0.17 0.10 0.00
## hsc2_T1  0.03 0.11 0.09 0.18 0.25 0.21 0.14 0.00
## hsc3_T1  0.00 0.04 0.04 0.11 0.20 0.31 0.31 0.01
## hsc4_T1  0.07 0.11 0.11 0.17 0.25 0.15 0.14 0.00
## hsc6_T1  0.02 0.04 0.11 0.18 0.31 0.22 0.12 0.00
## hsc7_T1  0.04 0.10 0.11 0.18 0.15 0.23 0.19 0.00
## hsc8_T1  0.03 0.14 0.11 0.37 0.21 0.11 0.04 0.00
## hsc9_T1  0.08 0.18 0.16 0.33 0.15 0.09 0.01 0.00
## hsc10_T1 0.00 0.00 0.00 0.03 0.09 0.25 0.63 0.00
## hsc11_T1 0.02 0.04 0.11 0.22 0.23 0.20 0.18 0.00
## hsc12_T1 0.02 0.04 0.04 0.02 0.25 0.32 0.31 0.00

omega(data[, c(6,7,8,9,11,12,13,14,15,16,17)],3,fm="ml") #omega hierarchical=.41, omega total=.74

## Omega 
## Call: omega(m = data[, c(6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17)], 
##     nfactors = 3, fm = "ml")
## Alpha:                 0.63 
## G.6:                   0.69 
## Omega Hierarchical:    0.41 
## Omega H asymptotic:    0.56 
## Omega Total            0.74 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##              g   F1*   F2*   F3*   h2   u2   p2
## hsc1_T1                     0.98 1.00 0.00 0.03
## hsc2_T1   0.66        0.69       0.91 0.09 0.48
## hsc3_T1                          0.06 0.94 0.20
## hsc4_T1   0.30  0.32             0.22 0.78 0.42
## hsc6_T1   0.43  0.45             0.40 0.60 0.47
## hsc7_T1                          0.03 0.97 0.00
## hsc8_T1   0.56  0.67             0.76 0.24 0.41
## hsc9_T1   0.23  0.33             0.17 0.83 0.31
## hsc10_T1              0.25       0.10 0.90 0.09
## hsc11_T1  0.48        0.44       0.46 0.54 0.50
## hsc12_T1  0.23             -0.21 0.15 0.85 0.36
## 
## With eigenvalues of:
##    g  F1*  F2*  F3* 
## 1.41 0.98 0.76 1.09 
## 
## general/max  1.29   max/min =   1.43
## mean percent general =  0.3    with sd =  0.19 and cv of  0.63 
## Explained Common Variance of the general factor =  0.33 
## 
## The degrees of freedom are 25  and the fit is  0.24 
## The number of observations was  114  with Chi Square =  26.06  with prob <  0.4
## The root mean square of the residuals is  0.05 
## The df corrected root mean square of the residuals is  0.07
## RMSEA index =  0.031  and the 10 % confidence intervals are  0 0.079
## BIC =  -92.35
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44  and the fit is  0.99 
## The number of observations was  114  with Chi Square =  106.32  with prob <  4.5e-07
## The root mean square of the residuals is  0.12 
## The df corrected root mean square of the residuals is  0.13 
## 
## RMSEA index =  0.116  and the 10 % confidence intervals are  0.085 0.139
## BIC =  -102.07 
## 
## Measures of factor score adequacy             
##                                                  g  F1*  F2*  F3*
## Correlation of scores with factors            0.75 0.75 0.75 0.99
## Multiple R square of scores with factors      0.57 0.57 0.56 0.98
## Minimum correlation of factor score estimates 0.13 0.14 0.13 0.97
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*  F2*  F3*
## Omega total for total scores and subscales    0.74 0.61 0.69 0.45
## Omega general for total scores and subscales  0.41 0.24 0.31 0.10
## Omega group for total scores and subscales    0.26 0.37 0.38 0.35

#hsc_T2
alpha(data[, c(27,28,29,30,32,33,34,35,36,37,38)]) #alpha .78

## 
## Reliability analysis   
## Call: alpha(x = data[, c(27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N  ase mean   sd median_r
##       0.78      0.78    0.82      0.24 3.5 0.03  4.9 0.81     0.23
## 
##  lower alpha upper     95% confidence boundaries
## 0.72 0.78 0.84 
## 
##  Reliability if an item is dropped:
##          raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T2       0.77      0.77    0.81      0.25 3.4    0.031 0.032  0.24
## hsc2_T2       0.75      0.75    0.79      0.23 3.0    0.035 0.027  0.20
## hsc3_T2       0.78      0.79    0.82      0.27 3.7    0.029 0.030  0.25
## hsc4_T2       0.75      0.76    0.80      0.24 3.1    0.033 0.031  0.20
## hsc6_T2       0.73      0.73    0.77      0.21 2.7    0.037 0.024  0.20
## hsc7_T2       0.79      0.79    0.83      0.27 3.7    0.028 0.030  0.26
## hsc8_T2       0.74      0.74    0.78      0.22 2.9    0.036 0.028  0.20
## hsc9_T2       0.77      0.77    0.82      0.25 3.4    0.031 0.029  0.23
## hsc10_T2      0.78      0.78    0.82      0.27 3.6    0.030 0.031  0.25
## hsc11_T2      0.75      0.75    0.79      0.23 3.0    0.034 0.029  0.20
## hsc12_T2      0.75      0.75    0.79      0.23 2.9    0.034 0.033  0.18
## 
##  Item statistics 
##            n raw.r std.r r.cor r.drop mean   sd
## hsc1_T2  100  0.49  0.49  0.42   0.36  4.7 1.28
## hsc2_T2  100  0.67  0.66  0.65   0.56  4.8 1.48
## hsc3_T2  100  0.33  0.38  0.28   0.20  5.6 1.29
## hsc4_T2  100  0.63  0.61  0.57   0.49  4.5 1.74
## hsc6_T2  100  0.77  0.77  0.78   0.69  4.8 1.39
## hsc7_T2  100  0.36  0.34  0.22   0.18  4.8 1.71
## hsc8_T2   99  0.74  0.71  0.71   0.63  4.3 1.34
## hsc9_T2  100  0.50  0.48  0.40   0.35  3.8 1.51
## hsc10_T2 100  0.33  0.39  0.29   0.23  6.3 0.98
## hsc11_T2  99  0.66  0.64  0.62   0.54  4.7 1.58
## hsc12_T2 100  0.67  0.67  0.64   0.56  5.5 1.38
## 
## Non missing response frequency for each item
##             1    2    3    4    5    6    7 miss
## hsc1_T2  0.02 0.04 0.10 0.20 0.37 0.22 0.05 0.12
## hsc2_T2  0.01 0.08 0.12 0.13 0.30 0.24 0.12 0.12
## hsc3_T2  0.01 0.02 0.03 0.11 0.22 0.33 0.28 0.12
## hsc4_T2  0.07 0.11 0.10 0.11 0.27 0.25 0.09 0.12
## hsc6_T2  0.01 0.08 0.08 0.13 0.40 0.20 0.10 0.12
## hsc7_T2  0.03 0.09 0.09 0.23 0.10 0.27 0.19 0.12
## hsc8_T2  0.00 0.11 0.16 0.23 0.31 0.13 0.05 0.13
## hsc9_T2  0.04 0.20 0.15 0.30 0.17 0.09 0.05 0.12
## hsc10_T2 0.00 0.01 0.02 0.01 0.12 0.31 0.53 0.12
## hsc11_T2 0.02 0.10 0.11 0.14 0.30 0.18 0.14 0.13
## hsc12_T2 0.01 0.05 0.03 0.09 0.22 0.36 0.24 0.12

omega(data[, c(27,28,29,30,32,33,34,35,36,37,38)],3,fm="ml") #omega hierarchical=.64, omega total=.84

## Omega 
## Call: omega(m = data[, c(27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38)], 
##     nfactors = 3, fm = "ml")
## Alpha:                 0.78 
## G.6:                   0.82 
## Omega Hierarchical:    0.64 
## Omega H asymptotic:    0.77 
## Omega Total            0.84 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##              g   F1*   F2*   F3*   h2   u2   p2
## hsc1_T2   0.29        0.25       0.16 0.84 0.53
## hsc2_T2   0.57        0.72       0.84 0.16 0.38
## hsc3_T2                     0.51 0.29 0.71 0.08
## hsc4_T2   0.52  0.21             0.32 0.68 0.86
## hsc6_T2   0.85  0.33             0.83 0.17 0.87
## hsc7_T2                          0.06 0.94 0.36
## hsc8_T2   0.71  0.26             0.57 0.43 0.88
## hsc9_T2   0.48  0.22       -0.21 0.32 0.68 0.72
## hsc10_T2                    0.66 0.47 0.53 0.06
## hsc11_T2  0.50        0.55       0.56 0.44 0.46
## hsc12_T2  0.56              0.30 0.44 0.56 0.72
## 
## With eigenvalues of:
##    g  F1*  F2*  F3* 
## 2.77 0.29 0.89 0.89 
## 
## general/max  3.11   max/min =   3.03
## mean percent general =  0.54    with sd =  0.3 and cv of  0.56 
## Explained Common Variance of the general factor =  0.57 
## 
## The degrees of freedom are 25  and the fit is  0.45 
## The number of observations was  114  with Chi Square =  48.23  with prob <  0.0035
## The root mean square of the residuals is  0.06 
## The df corrected root mean square of the residuals is  0.08
## RMSEA index =  0.096  and the 10 % confidence intervals are  0.051 0.129
## BIC =  -70.17
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44  and the fit is  1.16 
## The number of observations was  114  with Chi Square =  124.99  with prob <  1.1e-09
## The root mean square of the residuals is  0.11 
## The df corrected root mean square of the residuals is  0.12 
## 
## RMSEA index =  0.132  and the 10 % confidence intervals are  0.102 0.154
## BIC =  -83.4 
## 
## Measures of factor score adequacy             
##                                                  g   F1*  F2*  F3*
## Correlation of scores with factors            0.89  0.40 0.84 0.77
## Multiple R square of scores with factors      0.79  0.16 0.71 0.59
## Minimum correlation of factor score estimates 0.59 -0.67 0.41 0.18
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*  F2*  F3*
## Omega total for total scores and subscales    0.84 0.79 0.74 0.57
## Omega general for total scores and subscales  0.64 0.68 0.33 0.16
## Omega group for total scores and subscales    0.16 0.11 0.41 0.42

#hsc_T3
alpha(data[, c(48,49,50,51,53,54,55,56,57,58,59)]) #alpha .75

## Some items ( hsc3_T3 hsc10_T3 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

## 
## Reliability analysis   
## Call: alpha(x = data[, c(48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.75      0.73    0.79       0.2 2.8 0.033  5.1 0.72     0.24
## 
##  lower alpha upper     95% confidence boundaries
## 0.68 0.75 0.81 
## 
##  Reliability if an item is dropped:
##          raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T3       0.74      0.73    0.79      0.21 2.6    0.033 0.058  0.25
## hsc2_T3       0.69      0.68    0.74      0.18 2.1    0.040 0.042  0.22
## hsc3_T3       0.77      0.76    0.80      0.24 3.2    0.030 0.045  0.25
## hsc4_T3       0.72      0.71    0.77      0.19 2.4    0.036 0.054  0.21
## hsc6_T3       0.69      0.67    0.73      0.17 2.0    0.041 0.042  0.21
## hsc7_T3       0.75      0.73    0.79      0.21 2.7    0.032 0.056  0.24
## hsc8_T3       0.69      0.67    0.74      0.17 2.1    0.042 0.038  0.21
## hsc9_T3       0.75      0.73    0.79      0.21 2.7    0.033 0.053  0.24
## hsc10_T3      0.76      0.76    0.79      0.24 3.1    0.031 0.044  0.25
## hsc11_T3      0.70      0.69    0.75      0.18 2.2    0.039 0.045  0.22
## hsc12_T3      0.73      0.71    0.76      0.19 2.4    0.035 0.056  0.24
## 
##  Item statistics 
##            n raw.r std.r r.cor r.drop mean   sd
## hsc1_T3  105  0.42  0.45 0.342  0.288  4.8 1.22
## hsc2_T3  105  0.74  0.71 0.725  0.641  5.1 1.45
## hsc3_T3  105  0.11  0.19 0.054 -0.026  6.0 1.10
## hsc4_T3  105  0.59  0.57 0.509  0.455  5.0 1.45
## hsc6_T3  104  0.77  0.76 0.782  0.690  5.3 1.31
## hsc7_T3  105  0.49  0.43 0.312  0.284  4.8 1.82
## hsc8_T3  105  0.78  0.75 0.777  0.694  4.7 1.46
## hsc9_T3  105  0.45  0.42 0.304  0.280  3.9 1.49
## hsc10_T3 105  0.12  0.22 0.112  0.011  6.4 0.85
## hsc11_T3 105  0.70  0.67 0.672  0.577  4.9 1.50
## hsc12_T3 105  0.52  0.57 0.523  0.419  5.7 1.06
## 
## Non missing response frequency for each item
##             1    2    3    4    5    6    7 miss
## hsc1_T3  0.01 0.03 0.11 0.18 0.38 0.23 0.06 0.08
## hsc2_T3  0.02 0.05 0.09 0.10 0.27 0.33 0.14 0.08
## hsc3_T3  0.00 0.01 0.02 0.06 0.22 0.28 0.42 0.08
## hsc4_T3  0.03 0.07 0.03 0.16 0.35 0.23 0.13 0.08
## hsc6_T3  0.02 0.02 0.05 0.13 0.30 0.32 0.16 0.09
## hsc7_T3  0.05 0.10 0.08 0.21 0.12 0.22 0.22 0.08
## hsc8_T3  0.02 0.05 0.14 0.22 0.28 0.17 0.12 0.08
## hsc9_T3  0.06 0.14 0.15 0.28 0.24 0.10 0.04 0.08
## hsc10_T3 0.00 0.00 0.01 0.03 0.10 0.28 0.59 0.08
## hsc11_T3 0.03 0.04 0.10 0.21 0.24 0.24 0.15 0.08
## hsc12_T3 0.00 0.01 0.02 0.09 0.26 0.37 0.26 0.08

omega(data[, c(48,49,50,51,53,54,55,56,57,58,59)],3,fm="ml") #omega hierarchical=.59, omega total=.83

## Omega 
## Call: omega(m = data[, c(48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59)], 
##     nfactors = 3, fm = "ml")
## Alpha:                 0.75 
## G.6:                   0.8 
## Omega Hierarchical:    0.59 
## Omega H asymptotic:    0.71 
## Omega Total            0.83 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##               g   F1*   F2*   F3*   h2   u2   p2
## hsc1_T3    0.28                   0.11 0.89 0.68
## hsc2_T3    0.96                   0.92 0.08 0.99
## hsc3_T3-                    -0.41 0.17 0.83 0.03
## hsc4_T3    0.32  0.42             0.29 0.71 0.36
## hsc6_T3    0.57  0.71             0.83 0.17 0.39
## hsc7_T3    0.27                   0.10 0.90 0.70
## hsc8_T3    0.70  0.45             0.70 0.30 0.71
## hsc9_T3    0.32                   0.15 0.85 0.67
## hsc10_T3-                   -0.99 1.00 0.00 0.01
## hsc11_T3   0.73                   0.55 0.45 0.97
## hsc12_T3   0.24  0.44        0.28 0.35 0.65 0.16
## 
## With eigenvalues of:
##    g  F1*  F2*  F3* 
## 2.70 1.19 0.01 1.29 
## 
## general/max  2.09   max/min =   162.31
## mean percent general =  0.52    with sd =  0.35 and cv of  0.68 
## Explained Common Variance of the general factor =  0.52 
## 
## The degrees of freedom are 25  and the fit is  0.26 
## The number of observations was  114  with Chi Square =  27.85  with prob <  0.31
## The root mean square of the residuals is  0.04 
## The df corrected root mean square of the residuals is  0.06
## RMSEA index =  0.04  and the 10 % confidence intervals are  0 0.084
## BIC =  -90.55
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44  and the fit is  1.38 
## The number of observations was  114  with Chi Square =  148.56  with prob <  2.9e-13
## The root mean square of the residuals is  0.13 
## The df corrected root mean square of the residuals is  0.14 
## 
## RMSEA index =  0.15  and the 10 % confidence intervals are  0.12 0.171
## BIC =  -59.83 
## 
## Measures of factor score adequacy             
##                                                  g  F1*   F2* F3*
## Correlation of scores with factors            0.97 0.88  0.08   1
## Multiple R square of scores with factors      0.93 0.78  0.01   1
## Minimum correlation of factor score estimates 0.86 0.56 -0.99   1
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*  F2*  F3*
## Omega total for total scores and subscales    0.83 0.79 0.92 0.71
## Omega general for total scores and subscales  0.59 0.49 0.92 0.01
## Omega group for total scores and subscales    0.26 0.29 0.00 0.70

#hsc_T4
alpha(data[, c(69,70,71,72,74,75,76,77,78,79,80)]) #alpha .79

## 
## Reliability analysis   
## Call: alpha(x = data[, c(69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean  sd median_r
##       0.79      0.79    0.84      0.25 3.7 0.028    5 0.8     0.22
## 
##  lower alpha upper     95% confidence boundaries
## 0.74 0.79 0.85 
## 
##  Reliability if an item is dropped:
##          raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T4       0.79      0.79    0.84      0.27 3.7    0.028 0.042  0.24
## hsc2_T4       0.77      0.76    0.81      0.24 3.2    0.031 0.036  0.22
## hsc3_T4       0.80      0.80    0.83      0.28 3.9    0.027 0.033  0.24
## hsc4_T4       0.76      0.76    0.81      0.24 3.1    0.032 0.040  0.19
## hsc6_T4       0.75      0.74    0.79      0.23 2.9    0.034 0.032  0.20
## hsc7_T4       0.79      0.78    0.83      0.26 3.6    0.028 0.042  0.22
## hsc8_T4       0.75      0.74    0.79      0.22 2.9    0.034 0.027  0.20
## hsc9_T4       0.78      0.78    0.83      0.26 3.5    0.029 0.042  0.20
## hsc10_T4      0.79      0.79    0.82      0.27 3.7    0.028 0.038  0.24
## hsc11_T4      0.77      0.76    0.81      0.24 3.2    0.031 0.035  0.21
## hsc12_T4      0.75      0.75    0.80      0.23 2.9    0.033 0.036  0.19
## 
##  Item statistics 
##            n raw.r std.r r.cor r.drop mean   sd
## hsc1_T4  106  0.37  0.41  0.29   0.25  4.8 1.17
## hsc2_T4  106  0.61  0.60  0.57   0.49  5.0 1.44
## hsc3_T4  106  0.28  0.34  0.26   0.15  5.9 1.19
## hsc4_T4  106  0.66  0.65  0.61   0.54  4.8 1.58
## hsc6_T4  106  0.75  0.74  0.75   0.66  5.0 1.45
## hsc7_T4  106  0.50  0.47  0.37   0.33  4.9 1.73
## hsc8_T4  106  0.77  0.75  0.76   0.69  4.6 1.42
## hsc9_T4  106  0.50  0.48  0.39   0.36  3.9 1.45
## hsc10_T4 106  0.36  0.43  0.36   0.26  6.3 0.96
## hsc11_T4 106  0.64  0.62  0.60   0.52  4.8 1.57
## hsc12_T4 106  0.73  0.73  0.71   0.65  5.6 1.35
## 
## Non missing response frequency for each item
##             1    2    3    4    5    6    7 miss
## hsc1_T4  0.01 0.02 0.14 0.16 0.39 0.25 0.03 0.07
## hsc2_T4  0.00 0.08 0.10 0.08 0.30 0.30 0.12 0.07
## hsc3_T4  0.00 0.02 0.02 0.08 0.20 0.27 0.41 0.07
## hsc4_T4  0.05 0.05 0.12 0.11 0.33 0.21 0.13 0.07
## hsc6_T4  0.02 0.08 0.08 0.08 0.36 0.27 0.11 0.07
## hsc7_T4  0.04 0.08 0.10 0.15 0.17 0.25 0.20 0.07
## hsc8_T4  0.02 0.05 0.16 0.19 0.32 0.16 0.10 0.07
## hsc9_T4  0.06 0.12 0.18 0.28 0.23 0.10 0.03 0.07
## hsc10_T4 0.00 0.01 0.01 0.03 0.10 0.28 0.57 0.07
## hsc11_T4 0.02 0.09 0.10 0.17 0.26 0.21 0.14 0.07
## hsc12_T4 0.01 0.05 0.02 0.07 0.29 0.29 0.27 0.07

omega(data[, c(69,70,71,72,74,75,76,77,78,79,80)],3,fm="ml") #omega hierarchical=.71, omega total=.86

## Omega 
## Call: omega(m = data[, c(69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80)], 
##     nfactors = 3, fm = "ml")
## Alpha:                 0.79 
## G.6:                   0.84 
## Omega Hierarchical:    0.71 
## Omega H asymptotic:    0.83 
## Omega Total            0.86 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##              g   F1*   F2*   F3*   h2   u2   p2
## hsc1_T4   0.22              0.24 0.10 0.90 0.46
## hsc2_T4   0.46        0.79       0.83 0.17 0.25
## hsc3_T4                     0.74 0.55 0.45 0.01
## hsc4_T4   0.63                   0.43 0.57 0.94
## hsc6_T4   0.83                   0.70 0.30 0.99
## hsc7_T4   0.39                   0.16 0.84 0.96
## hsc8_T4   0.84        0.23       0.80 0.20 0.89
## hsc9_T4   0.36                   0.14 0.86 0.93
## hsc10_T4                    0.77 0.61 0.39 0.05
## hsc11_T4  0.50        0.55       0.56 0.44 0.45
## hsc12_T4  0.74                   0.56 0.44 0.98
## 
## With eigenvalues of:
##    g  F1*  F2*  F3* 
## 3.17 0.01 1.01 1.24 
## 
## general/max  2.56   max/min =   95.33
## mean percent general =  0.63    with sd =  0.39 and cv of  0.62 
## Explained Common Variance of the general factor =  0.58 
## 
## The degrees of freedom are 25  and the fit is  0.33 
## The number of observations was  114  with Chi Square =  35.47  with prob <  0.08
## The root mean square of the residuals is  0.04 
## The df corrected root mean square of the residuals is  0.06
## RMSEA index =  0.067  and the 10 % confidence intervals are  0 0.104
## BIC =  -82.93
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44  and the fit is  1.41 
## The number of observations was  114  with Chi Square =  152.05  with prob <  8.2e-14
## The root mean square of the residuals is  0.12 
## The df corrected root mean square of the residuals is  0.14 
## 
## RMSEA index =  0.152  and the 10 % confidence intervals are  0.122 0.173
## BIC =  -56.35 
## 
## Measures of factor score adequacy             
##                                                  g   F1*  F2*  F3*
## Correlation of scores with factors            0.94  0.07 0.88 0.87
## Multiple R square of scores with factors      0.88  0.01 0.78 0.75
## Minimum correlation of factor score estimates 0.76 -0.99 0.55 0.50
## 
##  Total, General and Subset omega for each subset
##                                                  g F1*  F2*  F3*
## Omega total for total scores and subscales    0.86 0.7 0.78 0.69
## Omega general for total scores and subscales  0.71 0.7 0.64 0.21
## Omega group for total scores and subscales    0.14 0.0 0.14 0.48

#wb_T1
alpha(data[, c(18:22)]) #alpha .78

## 
## Reliability analysis   
## Call: alpha(x = data[, c(18:22)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.78      0.78    0.77      0.42 3.6 0.033  2.7 0.83     0.43
## 
##  lower alpha upper     95% confidence boundaries
## 0.71 0.78 0.84 
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T1      0.71      0.71    0.68      0.38 2.5    0.044 0.012  0.36
## wb2_T1      0.72      0.73    0.69      0.40 2.7    0.042 0.020  0.39
## wb3_T1      0.72      0.72    0.70      0.39 2.6    0.044 0.019  0.38
## wb4_T1      0.78      0.78    0.75      0.48 3.6    0.033 0.012  0.50
## wb5_T1      0.75      0.75    0.73      0.43 3.0    0.039 0.018  0.44
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean  sd
## wb1_T1 114  0.77  0.78  0.74   0.63  3.1 1.1
## wb2_T1 114  0.75  0.76  0.70   0.59  2.8 1.1
## wb3_T1 113  0.77  0.77  0.70   0.61  2.9 1.2
## wb4_T1 114  0.64  0.63  0.49   0.42  2.1 1.2
## wb5_T1 114  0.71  0.71  0.60   0.52  2.7 1.2
## 
## Non missing response frequency for each item
##           0    1    2    3    4    5 miss
## wb1_T1 0.01 0.05 0.24 0.34 0.27 0.09 0.00
## wb2_T1 0.02 0.11 0.27 0.35 0.20 0.05 0.00
## wb3_T1 0.02 0.10 0.23 0.32 0.25 0.09 0.01
## wb4_T1 0.04 0.31 0.30 0.24 0.08 0.04 0.00
## wb5_T1 0.03 0.13 0.25 0.33 0.19 0.06 0.00

omega(data[, c(18:22)],1,fm="ml") #omega hierarchical=.78, omega total=.79

## Omega_h for 1 factor is not meaningful, just omega_t

## Omega 
## Call: omega(m = data[, c(18:22)], nfactors = 1, fm = "ml")
## Alpha:                 0.78 
## G.6:                   0.77 
## Omega Hierarchical:    0.78 
## Omega H asymptotic:    0.99 
## Omega Total            0.79 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##           g  F1*   h2   u2 p2
## wb1_T1 0.79      0.62 0.38  1
## wb2_T1 0.68      0.46 0.54  1
## wb3_T1 0.71      0.50 0.50  1
## wb4_T1 0.44      0.20 0.80  1
## wb5_T1 0.61      0.37 0.63  1
## 
## With eigenvalues of:
##   g F1* 
## 2.1 0.0 
## 
## general/max  1.933559e+16   max/min =   1
## mean percent general =  1    with sd =  0 and cv of  0 
## Explained Common Variance of the general factor =  1 
## 
## The degrees of freedom are 5  and the fit is  0.23 
## The number of observations was  114  with Chi Square =  25.49  with prob <  0.00011
## The root mean square of the residuals is  0.09 
## The df corrected root mean square of the residuals is  0.12
## RMSEA index =  0.194  and the 10 % confidence intervals are  0.121 0.267
## BIC =  1.8
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5  and the fit is  0.23 
## The number of observations was  114  with Chi Square =  25.49  with prob <  0.00011
## The root mean square of the residuals is  0.09 
## The df corrected root mean square of the residuals is  0.12 
## 
## RMSEA index =  0.194  and the 10 % confidence intervals are  0.121 0.267
## BIC =  1.8 
## 
## Measures of factor score adequacy             
##                                                  g F1*
## Correlation of scores with factors            0.90   0
## Multiple R square of scores with factors      0.81   0
## Minimum correlation of factor score estimates 0.63  -1
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*
## Omega total for total scores and subscales    0.79 0.78
## Omega general for total scores and subscales  0.78 0.78
## Omega group for total scores and subscales    0.00 0.00

#wb_T2
alpha(data[, c(39:43)]) #alpha .85

## 
## Reliability analysis   
## Call: alpha(x = data[, c(39:43)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.84      0.85    0.85      0.52 5.5 0.024    3 0.91     0.48
## 
##  lower alpha upper     95% confidence boundaries
## 0.79 0.84 0.89 
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## wb1_T2      0.80      0.80    0.77      0.50 4.0    0.031 0.0099  0.46
## wb2_T2      0.79      0.79    0.75      0.49 3.8    0.032 0.0081  0.47
## wb3_T2      0.81      0.81    0.81      0.52 4.4    0.030 0.0204  0.47
## wb4_T2      0.83      0.83    0.82      0.56 5.0    0.026 0.0132  0.54
## wb5_T2      0.82      0.83    0.83      0.55 4.9    0.027 0.0168  0.52
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean  sd
## wb1_T2 100  0.81  0.82  0.80   0.70  3.2 1.0
## wb2_T2  99  0.83  0.84  0.82   0.73  3.1 1.1
## wb3_T2 100  0.79  0.79  0.72   0.66  3.1 1.1
## wb4_T2 100  0.75  0.73  0.65   0.58  2.4 1.3
## wb5_T2 100  0.75  0.75  0.65   0.59  3.0 1.2
## 
## Non missing response frequency for each item
##           0    1    2    3    4    5 miss
## wb1_T2 0.00 0.05 0.23 0.30 0.33 0.09 0.12
## wb2_T2 0.00 0.07 0.23 0.28 0.32 0.09 0.13
## wb3_T2 0.00 0.10 0.16 0.36 0.25 0.13 0.12
## wb4_T2 0.02 0.26 0.29 0.23 0.12 0.08 0.12
## wb5_T2 0.00 0.12 0.26 0.28 0.21 0.13 0.12

omega(data[, c(39:43)],1,fm="ml") #omega hierarchical=.83, omega total=.85

## Omega_h for 1 factor is not meaningful, just omega_t

## Omega 
## Call: omega(m = data[, c(39:43)], nfactors = 1, fm = "ml")
## Alpha:                 0.85 
## G.6:                   0.85 
## Omega Hierarchical:    0.83 
## Omega H asymptotic:    0.98 
## Omega Total            0.85 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##           g  F1*   h2   u2 p2
## wb1_T2 0.84      0.71 0.29  1
## wb2_T2 0.86      0.74 0.26  1
## wb3_T2 0.65      0.43 0.57  1
## wb4_T2 0.63      0.40 0.60  1
## wb5_T2 0.60      0.36 0.64  1
## 
## With eigenvalues of:
##   g F1* 
## 2.6 0.0 
## 
## general/max  4.745101e+16   max/min =   1
## mean percent general =  1    with sd =  0 and cv of  0 
## Explained Common Variance of the general factor =  1 
## 
## The degrees of freedom are 5  and the fit is  0.43 
## The number of observations was  114  with Chi Square =  47.58  with prob <  4.3e-09
## The root mean square of the residuals is  0.09 
## The df corrected root mean square of the residuals is  0.13
## RMSEA index =  0.279  and the 10 % confidence intervals are  0.207 0.348
## BIC =  23.9
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5  and the fit is  0.43 
## The number of observations was  114  with Chi Square =  47.58  with prob <  4.3e-09
## The root mean square of the residuals is  0.09 
## The df corrected root mean square of the residuals is  0.13 
## 
## RMSEA index =  0.279  and the 10 % confidence intervals are  0.207 0.348
## BIC =  23.9 
## 
## Measures of factor score adequacy             
##                                                  g F1*
## Correlation of scores with factors            0.94   0
## Multiple R square of scores with factors      0.88   0
## Minimum correlation of factor score estimates 0.76  -1
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*
## Omega total for total scores and subscales    0.85 0.83
## Omega general for total scores and subscales  0.83 0.83
## Omega group for total scores and subscales    0.00 0.00

#wb_T3
alpha(data[, c(60:64)]) #alpha .85

## 
## Reliability analysis   
## Call: alpha(x = data[, c(60:64)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
##       0.85      0.85    0.85      0.54 5.8 0.022  2.9 0.94     0.51
## 
##  lower alpha upper     95% confidence boundaries
## 0.81 0.85 0.9 
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## wb1_T3      0.81      0.81    0.78      0.52 4.4    0.029 0.0065  0.50
## wb2_T3      0.81      0.82    0.77      0.53 4.4    0.028 0.0029  0.51
## wb3_T3      0.83      0.83    0.81      0.55 4.8    0.026 0.0105  0.51
## wb4_T3      0.83      0.83    0.81      0.55 4.9    0.027 0.0109  0.53
## wb5_T3      0.83      0.83    0.81      0.55 4.9    0.026 0.0102  0.52
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean  sd
## wb1_T3 105  0.82  0.82  0.78   0.71  3.3 1.1
## wb2_T3 105  0.82  0.81  0.78   0.70  3.0 1.2
## wb3_T3 105  0.78  0.78  0.71   0.65  3.2 1.2
## wb4_T3 105  0.79  0.78  0.70   0.65  2.3 1.3
## wb5_T3 105  0.77  0.77  0.69   0.64  2.7 1.1
## 
## Non missing response frequency for each item
##           0    1    2    3    4    5 miss
## wb1_T3 0.01 0.06 0.17 0.34 0.27 0.15 0.08
## wb2_T3 0.01 0.11 0.21 0.30 0.26 0.10 0.08
## wb3_T3 0.01 0.08 0.16 0.33 0.28 0.14 0.08
## wb4_T3 0.06 0.19 0.38 0.17 0.14 0.06 0.08
## wb5_T3 0.01 0.15 0.26 0.36 0.15 0.07 0.08

omega(data[, c(60:64)],1,fm="ml") #omega hierarchical=.85, omega total=.85

## Omega_h for 1 factor is not meaningful, just omega_t

## Omega 
## Call: omega(m = data[, c(60:64)], nfactors = 1, fm = "ml")
## Alpha:                 0.85 
## G.6:                   0.85 
## Omega Hierarchical:    0.85 
## Omega H asymptotic:    0.99 
## Omega Total            0.85 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##           g  F1*   h2   u2 p2
## wb1_T3 0.81      0.66 0.34  1
## wb2_T3 0.80      0.65 0.35  1
## wb3_T3 0.68      0.47 0.53  1
## wb4_T3 0.70      0.49 0.51  1
## wb5_T3 0.66      0.44 0.56  1
## 
## With eigenvalues of:
##   g F1* 
## 2.7 0.0 
## 
## general/max  Inf   max/min =   NaN
## mean percent general =  1    with sd =  0 and cv of  0 
## Explained Common Variance of the general factor =  1 
## 
## The degrees of freedom are 5  and the fit is  0.3 
## The number of observations was  114  with Chi Square =  32.82  with prob <  4.1e-06
## The root mean square of the residuals is  0.08 
## The df corrected root mean square of the residuals is  0.11
## RMSEA index =  0.226  and the 10 % confidence intervals are  0.153 0.297
## BIC =  9.14
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5  and the fit is  0.3 
## The number of observations was  114  with Chi Square =  32.82  with prob <  4.1e-06
## The root mean square of the residuals is  0.08 
## The df corrected root mean square of the residuals is  0.11 
## 
## RMSEA index =  0.226  and the 10 % confidence intervals are  0.153 0.297
## BIC =  9.14 
## 
## Measures of factor score adequacy             
##                                                  g F1*
## Correlation of scores with factors            0.93   0
## Multiple R square of scores with factors      0.86   0
## Minimum correlation of factor score estimates 0.73  -1
## 
##  Total, General and Subset omega for each subset
##                                                  g  F1*
## Omega total for total scores and subscales    0.85 0.85
## Omega general for total scores and subscales  0.85 0.85
## Omega group for total scores and subscales    0.00 0.00

#wb_T4
alpha(data[, c(81:85)]) #alpha .90

## 
## Reliability analysis   
## Call: alpha(x = data[, c(81:85)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean  sd median_r
##        0.9       0.9     0.9      0.63 8.7 0.016  2.9 1.1     0.63
## 
##  lower alpha upper     95% confidence boundaries
## 0.86 0.9 0.93 
## 
##  Reliability if an item is dropped:
##        raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
## wb1_T4      0.86      0.86    0.86      0.61 6.3    0.021 0.0115  0.58
## wb2_T4      0.87      0.87    0.84      0.62 6.6    0.021 0.0053  0.63
## wb3_T4      0.88      0.88    0.87      0.65 7.5    0.018 0.0076  0.63
## wb4_T4      0.88      0.88    0.86      0.64 7.2    0.019 0.0051  0.68
## wb5_T4      0.87      0.88    0.87      0.64 7.1    0.020 0.0123  0.64
## 
##  Item statistics 
##          n raw.r std.r r.cor r.drop mean  sd
## wb1_T4 106  0.86  0.87  0.83   0.79  3.1 1.1
## wb2_T4 106  0.86  0.86  0.84   0.77  2.8 1.3
## wb3_T4 106  0.81  0.81  0.75   0.70  3.1 1.2
## wb4_T4 106  0.83  0.83  0.79   0.73  2.3 1.3
## wb5_T4 106  0.84  0.83  0.77   0.73  2.9 1.3
## 
## Non missing response frequency for each item
##           0    1    2    3    4    5 miss
## wb1_T4 0.01 0.09 0.16 0.32 0.32 0.09 0.07
## wb2_T4 0.03 0.13 0.23 0.30 0.20 0.11 0.07
## wb3_T4 0.01 0.08 0.24 0.29 0.22 0.16 0.07
## wb4_T4 0.06 0.23 0.32 0.20 0.11 0.08 0.07
## wb5_T4 0.04 0.11 0.24 0.28 0.19 0.14 0.07

omega(data[, c(81:85)],1,fm="ml") #omega hierarchical=.90, omega total=.90

## Omega_h for 1 factor is not meaningful, just omega_t

## Omega 
## Call: omega(m = data[, c(81:85)], nfactors = 1, fm = "ml")
## Alpha:                 0.9 
## G.6:                   0.9 
## Omega Hierarchical:    0.9 
## Omega H asymptotic:    1 
## Omega Total            0.9 
## 
## Schmid Leiman Factor loadings greater than  0.2 
##           g  F1*   h2   u2 p2
## wb1_T4 0.84      0.71 0.29  1
## wb2_T4 0.84      0.71 0.29  1
## wb3_T4 0.74      0.55 0.45  1
## wb4_T4 0.79      0.62 0.38  1
## wb5_T4 0.77      0.59 0.41  1
## 
## With eigenvalues of:
##   g F1* 
## 3.2 0.0 
## 
## general/max  Inf   max/min =   NaN
## mean percent general =  1    with sd =  0 and cv of  0 
## Explained Common Variance of the general factor =  1 
## 
## The degrees of freedom are 5  and the fit is  0.43 
## The number of observations was  114  with Chi Square =  46.84  with prob <  6.1e-09
## The root mean square of the residuals is  0.08 
## The df corrected root mean square of the residuals is  0.11
## RMSEA index =  0.276  and the 10 % confidence intervals are  0.204 0.346
## BIC =  23.16
## 
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5  and the fit is  0.43 
## The number of observations was  114  with Chi Square =  46.84  with prob <  6.1e-09
## The root mean square of the residuals is  0.08 
## The df corrected root mean square of the residuals is  0.11 
## 
## RMSEA index =  0.276  and the 10 % confidence intervals are  0.204 0.346
## BIC =  23.16 
## 
## Measures of factor score adequacy             
##                                                  g F1*
## Correlation of scores with factors            0.95   0
## Multiple R square of scores with factors      0.90   0
## Minimum correlation of factor score estimates 0.80  -1
## 
##  Total, General and Subset omega for each subset
##                                                 g F1*
## Omega total for total scores and subscales    0.9 0.9
## Omega general for total scores and subscales  0.9 0.9
## Omega group for total scores and subscales    0.0 0.0

（2）相関係数の算出

cordata <- data %>% dplyr::select(hsc_T1,hsc_T2,hsc_T3,hsc_T4,wb_T1,wb_T2,wb_T3,wb_T4,ev_T1,ev_T2,ev_T3,ev_T4,hsc_onemonth,wb_onemonth,ev_onemonth) %>% drop_na()
head(cordata)

names(cordata)

##  [1] "hsc_T1"       "hsc_T2"       "hsc_T3"       "hsc_T4"      
##  [5] "wb_T1"        "wb_T2"        "wb_T3"        "wb_T4"       
##  [9] "ev_T1"        "ev_T2"        "ev_T3"        "ev_T4"       
## [13] "hsc_onemonth" "wb_onemonth"  "ev_onemonth"

cormat <- round(cor(cordata),2)
head(cormat)

##        hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## hsc_T1   1.00   0.71   0.70   0.59  0.04 -0.02 -0.06  0.06 -0.12  0.04
## hsc_T2   0.71   1.00   0.74   0.77 -0.03 -0.09 -0.06  0.02 -0.06 -0.04
## hsc_T3   0.70   0.74   1.00   0.81 -0.06 -0.06 -0.16 -0.07  0.02 -0.01
## hsc_T4   0.59   0.77   0.81   1.00 -0.22 -0.15 -0.26 -0.21 -0.08 -0.08
## wb_T1    0.04  -0.03  -0.06  -0.22  1.00  0.67  0.69  0.66  0.29  0.24
## wb_T2   -0.02  -0.09  -0.06  -0.15  0.67  1.00  0.75  0.66  0.20  0.34
##        ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## hsc_T1 -0.04  0.17         0.84        0.01        0.01
## hsc_T2 -0.04  0.29         0.91       -0.04        0.05
## hsc_T3 -0.17  0.22         0.91       -0.10        0.02
## hsc_T4 -0.03  0.25         0.90       -0.24        0.02
## wb_T1   0.12  0.13        -0.08        0.85        0.31
## wb_T2   0.09  0.13        -0.09        0.88        0.31

library(reshape2)

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
## 
##     smiths

melted_cormat <- melt(cormat)
head(melted_cormat)

ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
  geom_tile(color = "white")+
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1,1), space = "Lab", 
                       name="Pearson\nCorrelation") +
  theme_minimal()+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 12, hjust = 1))+
  coord_fixed()

Weekly_HSP_Report

Shuhei Iimura

2020/02/11

目次

（1）前処理

1-1. ローデータの読み込み

1-2. 下位尺度得点作成

1-3. 度数分布とヒストグラム

1-4. 記述統計量

1-5. 内的整合性

（2）相関係数の算出

分析は以上です

eoe_T1	n
1.8	2
2.2	1
2.4	2
2.6	1
2.8	1
3.0	1
3.2	3
3.4	5
3.6	2
3.8	10
4.0	6
4.2	14
4.4	6
4.6	7
4.8	11
5.0	9
5.2	10
5.4	5
5.6	6
5.8	5
6.0	4
6.4	3

eoe_T2	n
1.8	2
2.0	1
2.4	1
2.6	2
2.8	2
3.0	1
3.2	2
3.4	2
3.6	2
3.8	4
4.0	5
4.2	8
4.4	6
4.6	9
4.8	16
5.0	3
5.2	7
5.4	5
5.6	9
5.8	2
6.0	3
6.2	4
6.4	1
6.6	1
6.8	1
NA	15

eoe_T3	n
2.0	1
2.2	1
2.4	1
2.6	1
3.0	2
3.2	1
3.6	1
3.8	4
4.0	3
4.2	5
4.4	7
4.6	10
4.8	8
5.0	14
5.2	11
5.4	11
5.6	7
5.8	3
6.0	2
6.2	2
6.4	5
6.6	2
7.0	2
NA	10

eoe_T4	n
1.0	1
2.0	1
2.2	1
2.4	1
2.6	1
2.8	3
3.0	2
3.2	3
3.4	1
3.6	1
3.8	3
4.0	6
4.2	2
4.4	7
4.6	13
4.8	7
5.0	7
5.2	10
5.4	7
5.6	11
5.8	6
6.0	5
6.2	2
6.4	2
6.6	1
6.8	1
7.0	1
NA	8

lst_T3	n
1.0	1
1.5	1
2.0	2
2.5	1
3.0	8
3.5	5
4.0	13
4.5	7
5.0	17
5.5	13
6.0	19
6.5	7
7.0	11
NA	9

wb_T1	n
0.6	1
0.8	1
1.2	1
1.4	5
1.6	5
1.8	6
2.0	7
2.2	11
2.4	5
2.6	11
2.8	11
3.0	14
3.2	7
3.4	7
3.6	7
3.8	5
4.0	3
4.2	3
4.4	1
4.6	1
5.0	1
NA	1

wb_T2	n
1.0	1
1.2	1
1.4	1
1.6	5
1.8	6
2.0	4
2.2	7
2.4	7
2.6	8
2.8	2
3.0	15
3.2	10
3.4	7
3.6	2
3.8	7
4.0	4
4.2	2
4.4	5
4.6	2
5.0	3
NA	15

wb_T3	n
0.0	1
0.8	1
1.0	2
1.4	3
1.6	1
1.8	5
2.0	5
2.2	8
2.4	10
2.6	7
2.8	10
3.0	12
3.2	8
3.4	5
3.6	4
3.8	10
4.0	5
4.4	1
4.6	3
4.8	1
5.0	3
NA	9

wb_T4	n
0.2	1
0.6	1
0.8	2
1.0	2
1.2	3
1.4	1
1.6	2
1.8	4
2.0	8
2.2	8
2.4	6
2.6	13
2.8	8
3.0	3
3.2	10
3.4	7
3.6	4
3.8	3
4.0	5
4.2	5
4.4	2
4.6	2
5.0	6
NA	8

eoe_T1	n
1.8	2
2.2	1
2.4	2
2.6	1
2.8	1
3.0	1
3.2	3
3.4	5
3.6	2
3.8	10
4.0	6
4.2	14
4.4	6
4.6	7
4.8	11
5.0	9
5.2	10
5.4	5
5.6	6
5.8	5
6.0	4
6.4	3

eoe_T2	n
1.8	2
2.0	1
2.4	1
2.6	2
2.8	2
3.0	1
3.2	2
3.4	2
3.6	2
3.8	4
4.0	5
4.2	8
4.4	6
4.6	9
4.8	16
5.0	3
5.2	7
5.4	5
5.6	9
5.8	2
6.0	3
6.2	4
6.4	1
6.6	1
6.8	1
NA	15

eoe_T3	n
2.0	1
2.2	1
2.4	1
2.6	1
3.0	2
3.2	1
3.6	1
3.8	4
4.0	3
4.2	5
4.4	7
4.6	10
4.8	8
5.0	14
5.2	11
5.4	11
5.6	7
5.8	3
6.0	2
6.2	2
6.4	5
6.6	2
7.0	2
NA	10

eoe_T4	n
1.0	1
2.0	1
2.2	1
2.4	1
2.6	1
2.8	3
3.0	2
3.2	3
3.4	1
3.6	1
3.8	3
4.0	6
4.2	2
4.4	7
4.6	13
4.8	7
5.0	7
5.2	10
5.4	7
5.6	11
5.8	6
6.0	5
6.2	2
6.4	2
6.6	1
6.8	1
7.0	1
NA	8

lst_T3	n
1.0	1
1.5	1
2.0	2
2.5	1
3.0	8
3.5	5
4.0	13
4.5	7
5.0	17
5.5	13
6.0	19
6.5	7
7.0	11
NA	9

wb_T1	n
0.6	1
0.8	1
1.2	1
1.4	5
1.6	5
1.8	6
2.0	7
2.2	11
2.4	5
2.6	11
2.8	11
3.0	14
3.2	7
3.4	7
3.6	7
3.8	5
4.0	3
4.2	3
4.4	1
4.6	1
5.0	1
NA	1

wb_T2	n
1.0	1
1.2	1
1.4	1
1.6	5
1.8	6
2.0	4
2.2	7
2.4	7
2.6	8
2.8	2
3.0	15
3.2	10
3.4	7
3.6	2
3.8	7
4.0	4
4.2	2
4.4	5
4.6	2
5.0	3
NA	15

wb_T3	n
0.0	1
0.8	1
1.0	2
1.4	3
1.6	1
1.8	5
2.0	5
2.2	8
2.4	10
2.6	7
2.8	10
3.0	12
3.2	8
3.4	5
3.6	4
3.8	10
4.0	5
4.4	1
4.6	3
4.8	1
5.0	3
NA	9

wb_T4	n
0.2	1
0.6	1
0.8	2
1.0	2
1.2	3
1.4	1
1.6	2
1.8	4
2.0	8
2.2	8
2.4	6
2.6	13
2.8	8
3.0	3
3.2	10
3.4	7
3.6	4
3.8	3
4.0	5
4.2	5
4.4	2
4.6	2
5.0	6
NA	8

eoe_T1	n
1.8	2
2.2	1
2.4	2
2.6	1
2.8	1
3.0	1
3.2	3
3.4	5
3.6	2
3.8	10
4.0	6
4.2	14
4.4	6
4.6	7
4.8	11
5.0	9
5.2	10
5.4	5
5.6	6
5.8	5
6.0	4
6.4	3

eoe_T2	n
1.8	2
2.0	1
2.4	1
2.6	2
2.8	2
3.0	1
3.2	2
3.4	2
3.6	2
3.8	4
4.0	5
4.2	8
4.4	6
4.6	9
4.8	16
5.0	3
5.2	7
5.4	5
5.6	9
5.8	2
6.0	3
6.2	4
6.4	1
6.6	1
6.8	1
NA	15

eoe_T3	n
2.0	1
2.2	1
2.4	1
2.6	1
3.0	2
3.2	1
3.6	1
3.8	4
4.0	3
4.2	5
4.4	7
4.6	10
4.8	8
5.0	14
5.2	11
5.4	11
5.6	7
5.8	3
6.0	2
6.2	2
6.4	5
6.6	2
7.0	2
NA	10

eoe_T4	n
1.0	1
2.0	1
2.2	1
2.4	1
2.6	1
2.8	3
3.0	2
3.2	3
3.4	1
3.6	1
3.8	3
4.0	6
4.2	2
4.4	7
4.6	13
4.8	7
5.0	7
5.2	10
5.4	7
5.6	11
5.8	6
6.0	5
6.2	2
6.4	2
6.6	1
6.8	1
7.0	1
NA	8

lst_T3	n
1.0	1
1.5	1
2.0	2
2.5	1
3.0	8
3.5	5
4.0	13
4.5	7
5.0	17
5.5	13
6.0	19
6.5	7
7.0	11
NA	9

wb_T1	n
0.6	1
0.8	1
1.2	1
1.4	5
1.6	5
1.8	6
2.0	7
2.2	11
2.4	5
2.6	11
2.8	11
3.0	14
3.2	7
3.4	7
3.6	7
3.8	5
4.0	3
4.2	3
4.4	1
4.6	1
5.0	1
NA	1

wb_T2	n
1.0	1
1.2	1
1.4	1
1.6	5
1.8	6
2.0	4
2.2	7
2.4	7
2.6	8
2.8	2
3.0	15
3.2	10
3.4	7
3.6	2
3.8	7
4.0	4
4.2	2
4.4	5
4.6	2
5.0	3
NA	15

wb_T3	n
0.0	1
0.8	1
1.0	2
1.4	3
1.6	1
1.8	5
2.0	5
2.2	8
2.4	10
2.6	7
2.8	10
3.0	12
3.2	8
3.4	5
3.6	4
3.8	10
4.0	5
4.4	1
4.6	3
4.8	1
5.0	3
NA	9

wb_T4	n
0.2	1
0.6	1
0.8	2
1.0	2
1.2	3
1.4	1
1.6	2
1.8	4
2.0	8
2.2	8
2.4	6
2.6	13
2.8	8
3.0	3
3.2	10
3.4	7
3.6	4
3.8	3
4.0	5
4.2	5
4.4	2
4.6	2
5.0	6
NA	8